{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w84cR3AZIU0e"
      },
      "source": [
        "## **Assignment #2: Classification, Regression, Clustering, Evaluation**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sDxa7s952Ukh"
      },
      "source": [
        "`Version: April 2026`"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n7afdXdxIbLA"
      },
      "source": [
        "### **Overview**\n",
        "\n",
        "In this assignment, you'll level up your data science toolkit. While the first assignment focused on the data, on this one you will practice:\n",
        "\n",
        "- Classification models\n",
        "\n",
        "- Regression models\n",
        "\n",
        "- Feature Engineering\n",
        "\n",
        "- Evaluations\n",
        "\n",
        "You’ll go from raw data to insights by building a full modeling pipeline, enhancing your dataset, and training different models.\n",
        "\n",
        "This assignment will be completed individually."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lJAPMumvIUyW"
      },
      "source": [
        "### **Objectives**\n",
        "\n",
        "You’ll gain hands-on experience in:\n",
        "- Evaluation\n",
        "- Classification\n",
        "- Regression\n",
        "- Dataset preparation\n",
        "- Explore various data hubs\n",
        "- Engineering meaningful features\n",
        "- Communicating findings clearly - visually and verbally\n",
        "\n",
        "<br><br>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MwRmaJBiIjMR"
      },
      "source": [
        "### **Submission Guidelines**\n",
        "\n",
        "1. Please note that this assignmnet must be submitted alone.\n",
        "2. Submit the link to your HugingFace Model.\n",
        "\n",
        "Your HF model should include:\n",
        "- README file: explanations, visuals, insights, etc.\n",
        "- **Video**: Include the video of your presentation in the README file.\n",
        "- **Python Notebook**: upload a copy of this notebook, with all of your coding work. Do not submit a Colab link; include the `.ipynb` file in the HF model.\n",
        "- **ML Models:** Upload your models.\n",
        "\n",
        "Note: Students may be randomly chosen to present their work in a quick online session with the T.A., typically lasting ±10 minutes. Similar to Peer Review.\n",
        "\n",
        "<br><br>\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hD9SZmagIjOV"
      },
      "source": [
        "### **Evaluation Criteria**\n",
        "\n",
        "* **Data Handling & EDA (20%)**\n",
        "  Thoughtful and thorough data cleaning; handling of missing values, outliers, duplicates, and more; well-chosen visualizations; clear statistical summaries; use of EDA to guide modeling choices.\n",
        "\n",
        "* **Feature Engineering (20%)**\n",
        "  Creative and effective feature creation, transformation, encoding, selection, scaling, and more; integration of clustering results as features; clear explanation of feature choices and their impact.\n",
        "\n",
        "* **Model Training (20%)**\n",
        "  Appropriate selection of models; correct train/test split; reproducible code; logical modeling workflow with a solid baseline and improvements post-feature engineering. An iterative process.\n",
        "\n",
        "* **Evaluation & Interpretation (20%)**\n",
        "  Use of relevant evaluation metrics; structured model comparison; use of feature importance or visualizations to interpret results; clear discussion of what the model learned and how it performed.\n",
        "\n",
        "* **Presentation (20%)**\n",
        "  4–6 minute video with clear delivery; structured narrative; visuals that support the explanation; confident, professional communication of findings and lessons.\n",
        "\n",
        "* **Bonus (up to +10%)**\n",
        "  Extra work such as trying data science tools, creative visualizations, advanced hyper param tuning, interactive dashboards, and deeper business/domain insights.\n",
        "\n",
        "* **Late Submission (-10% per day)**\n",
        "  Assignments submitted after the deadline will receive a 10% penalty per day.\n",
        "\n",
        "<br><br>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h3vpVHSxIUwI"
      },
      "source": [
        "### **Additional Guidelines**\n",
        "\n",
        "- The first thing you should do is download a copy of this notebook to your drive.\n",
        "- Keep your dataset size manageable. If the dataset is too large, you can sample a subset.\n",
        "- Run on Colab (CPU is fine). Colab free is enough. No GPU needed.\n",
        "- You may use any Python package (scikit-learn, xgboost, lightgbm, catboost, etc.).\n",
        "- No SHAP required. Use `feature_importances`, and similar tools.\n",
        "- Make sure your results are reproducible (set **seeds** where needed).\n",
        "- Be thoughtful with your cluster features — only use them if they help!\n",
        "- Your presentation should tell a story; what worked, what didn’t, and why.\n",
        "- Be creative, but also rigorous."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7lTH1B5b5c12"
      },
      "source": [
        "### Assignment High-level Flow"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EK9fe2XygjgM"
      },
      "source": [
        "![image.png](data:image/png;base64,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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "acyYQrhPdEhB"
      },
      "source": [
        "imports"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TPaWKBWmdGNF"
      },
      "source": [
        "Set Seeds"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "INAizD1WeZcf"
      },
      "source": [
        "For Jupyter Notebooks"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UwSXkPGvecLK"
      },
      "source": [
        "Warnings"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "H9ZazAMOc5jC"
      },
      "outputs": [],
      "source": [
        "!pip install -q kaggle\n",
        "\n",
        "import os\n",
        "import zipfile\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import shap\n",
        "\n",
        "from google.colab import files\n",
        "\n",
        "from sklearn.model_selection import train_test_split, GridSearchCV\n",
        "from sklearn.compose import ColumnTransformer\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.preprocessing import OneHotEncoder, StandardScaler, FunctionTransformer\n",
        "from sklearn.impute import SimpleImputer\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n",
        "from sklearn.linear_model import LinearRegression, Ridge, LogisticRegression\n",
        "from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier\n",
        "from sklearn.ensemble import GradientBoostingRegressor\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.linear_model import RidgeCV, Lasso\n",
        "from sklearn.metrics import silhouette_score\n",
        "from sklearn.cluster import KMeans\n",
        "from sklearn.cluster import AgglomerativeClustering\n",
        "from sklearn.decomposition import PCA\n",
        "from sklearn.metrics import classification_report, confusion_matrix, ConfusionMatrixDisplay\n",
        "from sklearn.model_selection import RandomizedSearchCV\n",
        "from sklearn.metrics import precision_recall_curve, PrecisionRecallDisplay, average_precision_score\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.patches as mpatches\n",
        "from sklearn.model_selection import cross_val_score\n",
        "from sklearn.model_selection import KFold, cross_val_score\n",
        "from scipy import sparse\n",
        "from scipy.cluster.hierarchy import linkage, dendrogram\n",
        "import ipywidgets as widgets\n",
        "from IPython.display import display, clear_output\n",
        "\n",
        "\n",
        "import joblib\n",
        "from IPython.display import display as ipy_display\n",
        "from sklearn.model_selection import StratifiedKFold, cross_val_score\n",
        "RANDOM_STATE = 42\n",
        "from sklearn.dummy import DummyClassifier\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zBaCUY21dHeF"
      },
      "outputs": [],
      "source": [
        "import random\n",
        "\n",
        "SEED = 42\n",
        "\n",
        "RANDOM_STATE = 42\n",
        "\n",
        "np.random.seed(RANDOM_STATE)\n",
        "random.seed(RANDOM_STATE)\n",
        "os.environ['PYTHONHASHSEED'] = str(SEED)\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "requirements = \"\"\"pandas\n",
        "numpy\n",
        "scikit-learn\n",
        "joblib\n",
        "matplotlib\n",
        "seaborn\n",
        "\"\"\"\n",
        "\n",
        "with open(\"requirements.txt\", \"w\") as f:\n",
        "    f.write(requirements)\n",
        "\n",
        "print(\"requirements.txt created\")"
      ],
      "metadata": {
        "id": "Njc6LQepcYWP",
        "outputId": "dbab946d-404b-4e71-baf8-0853974bedbf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "requirements.txt created\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a_vsO0Q1IOMT"
      },
      "source": [
        "# **Part 1: Select a Regression Dataset**\n",
        "\n",
        "1. Choose a numeric & categorical tabular dataset. If you prefer, you may use  open-source datasets; [Hugginface](https://huggingface.co/datasets?task_categories=task_categories:tabular-classification&sort=trending), [Kaggle](https://www.kaggle.com/datasets?tags=13302-Classification&minUsabilityRating=8.00+or+higher), etc.\n",
        "\n",
        "2. Avoid choosing a \"basic\"/\"small\" dataset.\n",
        "      - 10K rows and more.\n",
        "      - 15 features and more.\n",
        "      - Mix of Numeric & Categorial features are a must.\n",
        "\n",
        "3. The Label (target variable) is numeric.\n",
        "\n",
        "4. Please submit your dataset [here](https://forms.gle/zS8aZbBzuBV2z7wZ7), to share it with the class so everyone can see.\n",
        "And make sure your chosen dataset is unique using this [link](https://docs.google.com/spreadsheets/d/1M8uojrzhSyVnOlSAJpzCKxrhWdzPR77k4x8Kxvr8VDk/edit?usp=sharing).\n",
        "\n",
        "      *Note: Due to their popularity, the following are datasets you may not choose.*\n",
        "      > - Iris dataset\n",
        "      > - Wine dataset\n",
        "      > - Titanic dataset\n",
        "      > - Boston Housing dataset\n",
        "      > - ImageNet, Cifar, CelebFaces, IMDB\n",
        "\n",
        "5. Choose a dataset with a combination of numeric and textual values. This way you would have enough information to work on.\n",
        "\n",
        "6. Briefly describe your chosen dataset (source, size, features) and the question you want to answer."
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Dataset Description**\n",
        "The original dataset contains **123,850 job postings** and **49 columns**, including both categorical and numeric variables.\n",
        "\n",
        "To keep the project computationally manageable in Colab, a reproducible sample of **30,000 postings** was used for modeling, with a fixed random seed."
      ],
      "metadata": {
        "id": "lUfjVeMPRrc9"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "This project examines which LinkedIn job posting characteristics are associated with higher engagement, measured by job views.\n",
        "My analysis combines exploratory data analysis, regression modeling, classification, and clustering to understand both the drivers of engagement and the limits of prediction using available posting-level features.\n",
        "My main research question is:\n",
        "**Which job posting characteristics are associated with higher engagement, and how well can engagement be predicted or classified using available LinkedIn job features?**"
      ],
      "metadata": {
        "id": "ekyCKjgfRzdK"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "OI0MZzohKwfE",
        "outputId": "e796d779-abc4-4d8b-95a9-ec458cf92ddc"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-e9614a43-a6d1-4583-ae95-21516a47c047\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-e9614a43-a6d1-4583-ae95-21516a47c047\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script>// Copyright 2017 Google LLC\n",
              "//\n",
              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
              "// you may not use this file except in compliance with the License.\n",
              "// You may obtain a copy of the License at\n",
              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
              "\n",
              "/**\n",
              " * @fileoverview Helpers for google.colab Python module.\n",
              " */\n",
              "(function(scope) {\n",
              "function span(text, styleAttributes = {}) {\n",
              "  const element = document.createElement('span');\n",
              "  element.textContent = text;\n",
              "  for (const key of Object.keys(styleAttributes)) {\n",
              "    element.style[key] = styleAttributes[key];\n",
              "  }\n",
              "  return element;\n",
              "}\n",
              "\n",
              "// Max number of bytes which will be uploaded at a time.\n",
              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
              "\n",
              "function _uploadFiles(inputId, outputId) {\n",
              "  const steps = uploadFilesStep(inputId, outputId);\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  // Cache steps on the outputElement to make it available for the next call\n",
              "  // to uploadFilesContinue from Python.\n",
              "  outputElement.steps = steps;\n",
              "\n",
              "  return _uploadFilesContinue(outputId);\n",
              "}\n",
              "\n",
              "// This is roughly an async generator (not supported in the browser yet),\n",
              "// where there are multiple asynchronous steps and the Python side is going\n",
              "// to poll for completion of each step.\n",
              "// This uses a Promise to block the python side on completion of each step,\n",
              "// then passes the result of the previous step as the input to the next step.\n",
              "function _uploadFilesContinue(outputId) {\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  const steps = outputElement.steps;\n",
              "\n",
              "  const next = steps.next(outputElement.lastPromiseValue);\n",
              "  return Promise.resolve(next.value.promise).then((value) => {\n",
              "    // Cache the last promise value to make it available to the next\n",
              "    // step of the generator.\n",
              "    outputElement.lastPromiseValue = value;\n",
              "    return next.value.response;\n",
              "  });\n",
              "}\n",
              "\n",
              "/**\n",
              " * Generator function which is called between each async step of the upload\n",
              " * process.\n",
              " * @param {string} inputId Element ID of the input file picker element.\n",
              " * @param {string} outputId Element ID of the output display.\n",
              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
              " */\n",
              "function* uploadFilesStep(inputId, outputId) {\n",
              "  const inputElement = document.getElementById(inputId);\n",
              "  inputElement.disabled = false;\n",
              "\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  outputElement.innerHTML = '';\n",
              "\n",
              "  const pickedPromise = new Promise((resolve) => {\n",
              "    inputElement.addEventListener('change', (e) => {\n",
              "      resolve(e.target.files);\n",
              "    });\n",
              "  });\n",
              "\n",
              "  const cancel = document.createElement('button');\n",
              "  inputElement.parentElement.appendChild(cancel);\n",
              "  cancel.textContent = 'Cancel upload';\n",
              "  const cancelPromise = new Promise((resolve) => {\n",
              "    cancel.onclick = () => {\n",
              "      resolve(null);\n",
              "    };\n",
              "  });\n",
              "\n",
              "  // Wait for the user to pick the files.\n",
              "  const files = yield {\n",
              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
              "    response: {\n",
              "      action: 'starting',\n",
              "    }\n",
              "  };\n",
              "\n",
              "  cancel.remove();\n",
              "\n",
              "  // Disable the input element since further picks are not allowed.\n",
              "  inputElement.disabled = true;\n",
              "\n",
              "  if (!files) {\n",
              "    return {\n",
              "      response: {\n",
              "        action: 'complete',\n",
              "      }\n",
              "    };\n",
              "  }\n",
              "\n",
              "  for (const file of files) {\n",
              "    const li = document.createElement('li');\n",
              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
              "    li.append(span(\n",
              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
              "        `last modified: ${\n",
              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
              "                                    'n/a'} - `));\n",
              "    const percent = span('0% done');\n",
              "    li.appendChild(percent);\n",
              "\n",
              "    outputElement.appendChild(li);\n",
              "\n",
              "    const fileDataPromise = new Promise((resolve) => {\n",
              "      const reader = new FileReader();\n",
              "      reader.onload = (e) => {\n",
              "        resolve(e.target.result);\n",
              "      };\n",
              "      reader.readAsArrayBuffer(file);\n",
              "    });\n",
              "    // Wait for the data to be ready.\n",
              "    let fileData = yield {\n",
              "      promise: fileDataPromise,\n",
              "      response: {\n",
              "        action: 'continue',\n",
              "      }\n",
              "    };\n",
              "\n",
              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
              "    let position = 0;\n",
              "    do {\n",
              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
              "      const chunk = new Uint8Array(fileData, position, length);\n",
              "      position += length;\n",
              "\n",
              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
              "      yield {\n",
              "        response: {\n",
              "          action: 'append',\n",
              "          file: file.name,\n",
              "          data: base64,\n",
              "        },\n",
              "      };\n",
              "\n",
              "      let percentDone = fileData.byteLength === 0 ?\n",
              "          100 :\n",
              "          Math.round((position / fileData.byteLength) * 100);\n",
              "      percent.textContent = `${percentDone}% done`;\n",
              "\n",
              "    } while (position < fileData.byteLength);\n",
              "  }\n",
              "\n",
              "  // All done.\n",
              "  yield {\n",
              "    response: {\n",
              "      action: 'complete',\n",
              "    }\n",
              "  };\n",
              "}\n",
              "\n",
              "scope.google = scope.google || {};\n",
              "scope.google.colab = scope.google.colab || {};\n",
              "scope.google.colab._files = {\n",
              "  _uploadFiles,\n",
              "  _uploadFilesContinue,\n",
              "};\n",
              "})(self);\n",
              "</script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving kaggle (1).json to kaggle (1) (1).json\n",
            "mv: cannot stat 'kaggle.json': No such file or directory\n",
            "chmod: cannot access '/root/.kaggle/kaggle.json': No such file or directory\n",
            "Dataset URL: https://www.kaggle.com/datasets/arshkon/linkedin-job-postings\n",
            "License(s): CC-BY-SA-4.0\n",
            "linkedin-job-postings.zip: Skipping, found more recently modified local copy (use --force to force download)\n",
            "Dataset downloaded and extracted.\n"
          ]
        }
      ],
      "source": [
        "# 1.1 — Setup and Data Loading\n",
        "uploaded = files.upload()\n",
        "\n",
        "!mkdir -p ~/.kaggle\n",
        "!mv kaggle.json ~/.kaggle/kaggle.json\n",
        "!chmod 600 ~/.kaggle/kaggle.json\n",
        "\n",
        "!kaggle datasets download -d arshkon/linkedin-job-postings\n",
        "\n",
        "with zipfile.ZipFile(\"linkedin-job-postings.zip\", \"r\") as zip_ref:\n",
        "    zip_ref.extractall(\"linkedin_data\")\n",
        "\n",
        "print(\"Dataset downloaded and extracted.\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rLRFkLVUYWzK",
        "outputId": "cb2cb6e1-dad4-44cb-9ad4-a2c2bc9716ef"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "linkedin_data/postings.csv\n",
            "linkedin_data/jobs/salaries.csv\n",
            "linkedin_data/jobs/job_skills.csv\n",
            "linkedin_data/jobs/job_industries.csv\n",
            "linkedin_data/jobs/benefits.csv\n",
            "linkedin_data/companies/company_industries.csv\n",
            "linkedin_data/companies/employee_counts.csv\n",
            "linkedin_data/companies/company_specialities.csv\n",
            "linkedin_data/companies/companies.csv\n",
            "linkedin_data/mappings/skills.csv\n",
            "linkedin_data/mappings/industries.csv\n"
          ]
        }
      ],
      "source": [
        "for root, dirs, files_list in os.walk(\"linkedin_data\"):\n",
        "    for file in files_list:\n",
        "        print(os.path.join(root, file))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 586
        },
        "id": "6oE0EHB1YPU3",
        "outputId": "76f08a46-a7ed-4bd7-cace-8f15891c2451"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Postings file: linkedin_data/postings.csv\n",
            "Companies file: linkedin_data/companies/companies.csv\n",
            "Working with a sample of 30000 rows for reproducibility.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       job_id                             company_name  \\\n",
              "0  3902944011                            Current Power   \n",
              "1  3901960222                             DISH Network   \n",
              "2  3900944095  Coca-Cola Bottling Company UNITED, Inc.   \n",
              "3  3903878594                        Denver7 (KMGH-TV)   \n",
              "4  3905670593                  BAYADA Home Health Care   \n",
              "\n",
              "                                        title  \\\n",
              "0  Senior Automation Engineer - Power Systems   \n",
              "1        DISH Installation Technician - Field   \n",
              "2                               Order Builder   \n",
              "3        Mountain Multimedia Journalist, KMGH   \n",
              "4              Licensed Practical Nurse (LPN)   \n",
              "\n",
              "                                         description  max_salary pay_period  \\\n",
              "0  The Senior Automation / Power Systems Engineer...         NaN        NaN   \n",
              "1  Company Summary\\n\\nDISH, an EchoStar Company, ...       19.75     HOURLY   \n",
              "2  Division: North Alabama\\n\\nDepartment : Oxford...         NaN        NaN   \n",
              "3  KMGH, the E.W. Scripps Company ABC affiliate i...         NaN        NaN   \n",
              "4  Come for the Flexibility, Stay for the Culture...       35.00     HOURLY   \n",
              "\n",
              "        location  company_id  views  med_salary  ...  skills_desc  \\\n",
              "0    Houston, TX    760913.0   22.0         NaN  ...          NaN   \n",
              "1     Orange, TX      4296.0    5.0         NaN  ...          NaN   \n",
              "2     Oxford, AL    136791.0    4.0         NaN  ...          NaN   \n",
              "3     Denver, CO  11500365.0    4.0         NaN  ...          NaN   \n",
              "4  Teterboro, NJ     19472.0    4.0         NaN  ...          NaN   \n",
              "\n",
              "    listed_time                 posting_domain  sponsored  work_type currency  \\\n",
              "0  1.713280e+12                            NaN          0  FULL_TIME      NaN   \n",
              "1  1.713478e+12                  jobs.dish.com          0  FULL_TIME      USD   \n",
              "2  1.713389e+12          careers.cokeonena.com          0  FULL_TIME      NaN   \n",
              "3  1.713496e+12  scripps.wd5.myworkdayjobs.com          0  FULL_TIME      NaN   \n",
              "4  1.713521e+12            jsv3.recruitics.com          0  FULL_TIME      USD   \n",
              "\n",
              "  compensation_type normalized_salary  zip_code     fips  \n",
              "0               NaN               NaN   77002.0  48201.0  \n",
              "1       BASE_SALARY           41080.0   77630.0  48361.0  \n",
              "2               NaN               NaN   36203.0   1015.0  \n",
              "3               NaN               NaN   80202.0   8031.0  \n",
              "4       BASE_SALARY           67600.0    7608.0  34003.0  \n",
              "\n",
              "[5 rows x 31 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ae25b839-fe23-419a-aa3d-73c8b1abbb13\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>job_id</th>\n",
              "      <th>company_name</th>\n",
              "      <th>title</th>\n",
              "      <th>description</th>\n",
              "      <th>max_salary</th>\n",
              "      <th>pay_period</th>\n",
              "      <th>location</th>\n",
              "      <th>company_id</th>\n",
              "      <th>views</th>\n",
              "      <th>med_salary</th>\n",
              "      <th>...</th>\n",
              "      <th>skills_desc</th>\n",
              "      <th>listed_time</th>\n",
              "      <th>posting_domain</th>\n",
              "      <th>sponsored</th>\n",
              "      <th>work_type</th>\n",
              "      <th>currency</th>\n",
              "      <th>compensation_type</th>\n",
              "      <th>normalized_salary</th>\n",
              "      <th>zip_code</th>\n",
              "      <th>fips</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3902944011</td>\n",
              "      <td>Current Power</td>\n",
              "      <td>Senior Automation Engineer - Power Systems</td>\n",
              "      <td>The Senior Automation / Power Systems Engineer...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Houston, TX</td>\n",
              "      <td>760913.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.713280e+12</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>FULL_TIME</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>77002.0</td>\n",
              "      <td>48201.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3901960222</td>\n",
              "      <td>DISH Network</td>\n",
              "      <td>DISH Installation Technician - Field</td>\n",
              "      <td>Company Summary\\n\\nDISH, an EchoStar Company, ...</td>\n",
              "      <td>19.75</td>\n",
              "      <td>HOURLY</td>\n",
              "      <td>Orange, TX</td>\n",
              "      <td>4296.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.713478e+12</td>\n",
              "      <td>jobs.dish.com</td>\n",
              "      <td>0</td>\n",
              "      <td>FULL_TIME</td>\n",
              "      <td>USD</td>\n",
              "      <td>BASE_SALARY</td>\n",
              "      <td>41080.0</td>\n",
              "      <td>77630.0</td>\n",
              "      <td>48361.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3900944095</td>\n",
              "      <td>Coca-Cola Bottling Company UNITED, Inc.</td>\n",
              "      <td>Order Builder</td>\n",
              "      <td>Division: North Alabama\\n\\nDepartment : Oxford...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Oxford, AL</td>\n",
              "      <td>136791.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.713389e+12</td>\n",
              "      <td>careers.cokeonena.com</td>\n",
              "      <td>0</td>\n",
              "      <td>FULL_TIME</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>36203.0</td>\n",
              "      <td>1015.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3903878594</td>\n",
              "      <td>Denver7 (KMGH-TV)</td>\n",
              "      <td>Mountain Multimedia Journalist, KMGH</td>\n",
              "      <td>KMGH, the E.W. Scripps Company ABC affiliate i...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Denver, CO</td>\n",
              "      <td>11500365.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.713496e+12</td>\n",
              "      <td>scripps.wd5.myworkdayjobs.com</td>\n",
              "      <td>0</td>\n",
              "      <td>FULL_TIME</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>80202.0</td>\n",
              "      <td>8031.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3905670593</td>\n",
              "      <td>BAYADA Home Health Care</td>\n",
              "      <td>Licensed Practical Nurse (LPN)</td>\n",
              "      <td>Come for the Flexibility, Stay for the Culture...</td>\n",
              "      <td>35.00</td>\n",
              "      <td>HOURLY</td>\n",
              "      <td>Teterboro, NJ</td>\n",
              "      <td>19472.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.713521e+12</td>\n",
              "      <td>jsv3.recruitics.com</td>\n",
              "      <td>0</td>\n",
              "      <td>FULL_TIME</td>\n",
              "      <td>USD</td>\n",
              "      <td>BASE_SALARY</td>\n",
              "      <td>67600.0</td>\n",
              "      <td>7608.0</td>\n",
              "      <td>34003.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 31 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ae25b839-fe23-419a-aa3d-73c8b1abbb13')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-ae25b839-fe23-419a-aa3d-73c8b1abbb13 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-ae25b839-fe23-419a-aa3d-73c8b1abbb13');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
            }
          },
          "metadata": {},
          "execution_count": 131
        }
      ],
      "source": [
        "postings_path = None\n",
        "companies_path = None\n",
        "\n",
        "for root, dirs, files_list in os.walk(\"linkedin_data\"):\n",
        "    for file in files_list:\n",
        "        full_path = os.path.join(root, file)\n",
        "        if file.lower() == \"postings.csv\":\n",
        "            postings_path = full_path\n",
        "        if file.lower() == \"companies.csv\":\n",
        "            companies_path = full_path\n",
        "\n",
        "print(\"Postings file:\", postings_path)\n",
        "print(\"Companies file:\", companies_path)\n",
        "\n",
        "df = pd.read_csv(postings_path)\n",
        "df = df.sample(n=30000, random_state=RANDOM_STATE).reset_index(drop=True)\n",
        "print(f\"Working with a sample of {len(df)} rows for reproducibility.\")\n",
        "\n",
        "if companies_path is not None:\n",
        "    companies = pd.read_csv(companies_path)\n",
        "else:\n",
        "    companies = None\n",
        "\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ID1Lv-0pYLvS",
        "outputId": "abceda11-7ee4-492e-9808-4e78b831eb9e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Company metadata joined successfully.\n",
            "Rows before join: 30000\n",
            "Rows after join: 30000\n",
            "Columns after join: 40\n",
            "Duplicate rows after join: 0\n"
          ]
        }
      ],
      "source": [
        "rows_before_join = df.shape[0]\n",
        "\n",
        "if companies is not None and \"company_id\" in df.columns and \"company_id\" in companies.columns:\n",
        "    df = df.merge(\n",
        "        companies,\n",
        "        on=\"company_id\",\n",
        "        how=\"left\",\n",
        "        suffixes=(\"\", \"_company\")\n",
        "    )\n",
        "    print(\"Company metadata joined successfully.\")\n",
        "else:\n",
        "    print(\"Company metadata was not joined.\")\n",
        "\n",
        "rows_after_join = df.shape[0]\n",
        "\n",
        "print(\"Rows before join:\", rows_before_join)\n",
        "print(\"Rows after join:\", rows_after_join)\n",
        "print(\"Columns after join:\", df.shape[1])\n",
        "print(\"Duplicate rows after join:\", df.duplicated().sum())"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Preliminary Data Overview\n",
        "\n",
        "Before cleaning or modeling, I first inspected the structure of the dataset.  \n",
        "This step checks the dataset shape, sample rows, data types, missing values, and basic descriptive statistics."
      ],
      "metadata": {
        "id": "jpvuVTPLT7w0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 1.2 Initial Data Inspection\n",
        "print(\"Dataset shape:\", df.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qFFCr2FnUVvr",
        "outputId": "7ee31a81-40cd-494e-e3b6-7d3dc380ec30"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset shape: (30000, 40)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\nFirst five rows:\")\n",
        "display(df.head())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 603
        },
        "id": "GJGcPqEiUZJW",
        "outputId": "763ea782-dc99-466a-8894-ef822190bcc5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "First five rows:\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "       job_id                             company_name  \\\n",
              "0  3902944011                            Current Power   \n",
              "1  3901960222                             DISH Network   \n",
              "2  3900944095  Coca-Cola Bottling Company UNITED, Inc.   \n",
              "3  3903878594                        Denver7 (KMGH-TV)   \n",
              "4  3905670593                  BAYADA Home Health Care   \n",
              "\n",
              "                                        title  \\\n",
              "0  Senior Automation Engineer - Power Systems   \n",
              "1        DISH Installation Technician - Field   \n",
              "2                               Order Builder   \n",
              "3        Mountain Multimedia Journalist, KMGH   \n",
              "4              Licensed Practical Nurse (LPN)   \n",
              "\n",
              "                                         description  max_salary pay_period  \\\n",
              "0  The Senior Automation / Power Systems Engineer...         NaN        NaN   \n",
              "1  Company Summary\\n\\nDISH, an EchoStar Company, ...       19.75     HOURLY   \n",
              "2  Division: North Alabama\\n\\nDepartment : Oxford...         NaN        NaN   \n",
              "3  KMGH, the E.W. Scripps Company ABC affiliate i...         NaN        NaN   \n",
              "4  Come for the Flexibility, Stay for the Culture...       35.00     HOURLY   \n",
              "\n",
              "        location  company_id  views  med_salary  ...     fips  \\\n",
              "0    Houston, TX    760913.0   22.0         NaN  ...  48201.0   \n",
              "1     Orange, TX      4296.0    5.0         NaN  ...  48361.0   \n",
              "2     Oxford, AL    136791.0    4.0         NaN  ...   1015.0   \n",
              "3     Denver, CO  11500365.0    4.0         NaN  ...   8031.0   \n",
              "4  Teterboro, NJ     19472.0    4.0         NaN  ...  34003.0   \n",
              "\n",
              "                                      name  \\\n",
              "0                            Current Power   \n",
              "1                             DISH Network   \n",
              "2  Coca-Cola Bottling Company UNITED, Inc.   \n",
              "3                        Denver7 (KMGH-TV)   \n",
              "4                  BAYADA Home Health Care   \n",
              "\n",
              "                                 description_company  company_size  \\\n",
              "0  Current Power is an International supplier of ...           1.0   \n",
              "1  Our adventure began by changing the way people...           7.0   \n",
              "2  Coca-Cola Bottling Company UNITED was founded ...           6.0   \n",
              "3  Founded in 1953 and acquired by the E.W. Scrip...           2.0   \n",
              "4  BAYADA Home Health Care was founded by J. Mark...           7.0   \n",
              "\n",
              "          state country                 city zip_code_company  \\\n",
              "0            TX      US              Houston            77066   \n",
              "1            CO      US            Englewood            80112   \n",
              "2  AL - Alabama      US           Birmingham            35217   \n",
              "3      Colorado      US               Denver                0   \n",
              "4    New Jersey      US  Pennsauken Township                0   \n",
              "\n",
              "                    address                                                url  \n",
              "0       5050 West Greens Rd  https://www.linkedin.com/company/current-power...  \n",
              "1     9601 S. Meridian Blvd      https://www.linkedin.com/company/dish-network  \n",
              "2  4600 East Lake Boulevard  https://www.linkedin.com/company/coca-cola-bot...  \n",
              "3          123 E Speer Blvd           https://www.linkedin.com/company/denver7  \n",
              "4       4300 Haddonfield Rd  https://www.linkedin.com/company/bayada-home-h...  \n",
              "\n",
              "[5 rows x 40 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-2beeaa05-bee9-4a1b-8397-71e7e6acebeb\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>job_id</th>\n",
              "      <th>company_name</th>\n",
              "      <th>title</th>\n",
              "      <th>description</th>\n",
              "      <th>max_salary</th>\n",
              "      <th>pay_period</th>\n",
              "      <th>location</th>\n",
              "      <th>company_id</th>\n",
              "      <th>views</th>\n",
              "      <th>med_salary</th>\n",
              "      <th>...</th>\n",
              "      <th>fips</th>\n",
              "      <th>name</th>\n",
              "      <th>description_company</th>\n",
              "      <th>company_size</th>\n",
              "      <th>state</th>\n",
              "      <th>country</th>\n",
              "      <th>city</th>\n",
              "      <th>zip_code_company</th>\n",
              "      <th>address</th>\n",
              "      <th>url</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3902944011</td>\n",
              "      <td>Current Power</td>\n",
              "      <td>Senior Automation Engineer - Power Systems</td>\n",
              "      <td>The Senior Automation / Power Systems Engineer...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Houston, TX</td>\n",
              "      <td>760913.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>48201.0</td>\n",
              "      <td>Current Power</td>\n",
              "      <td>Current Power is an International supplier of ...</td>\n",
              "      <td>1.0</td>\n",
              "      <td>TX</td>\n",
              "      <td>US</td>\n",
              "      <td>Houston</td>\n",
              "      <td>77066</td>\n",
              "      <td>5050 West Greens Rd</td>\n",
              "      <td>https://www.linkedin.com/company/current-power...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3901960222</td>\n",
              "      <td>DISH Network</td>\n",
              "      <td>DISH Installation Technician - Field</td>\n",
              "      <td>Company Summary\\n\\nDISH, an EchoStar Company, ...</td>\n",
              "      <td>19.75</td>\n",
              "      <td>HOURLY</td>\n",
              "      <td>Orange, TX</td>\n",
              "      <td>4296.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>48361.0</td>\n",
              "      <td>DISH Network</td>\n",
              "      <td>Our adventure began by changing the way people...</td>\n",
              "      <td>7.0</td>\n",
              "      <td>CO</td>\n",
              "      <td>US</td>\n",
              "      <td>Englewood</td>\n",
              "      <td>80112</td>\n",
              "      <td>9601 S. Meridian Blvd</td>\n",
              "      <td>https://www.linkedin.com/company/dish-network</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3900944095</td>\n",
              "      <td>Coca-Cola Bottling Company UNITED, Inc.</td>\n",
              "      <td>Order Builder</td>\n",
              "      <td>Division: North Alabama\\n\\nDepartment : Oxford...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Oxford, AL</td>\n",
              "      <td>136791.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>1015.0</td>\n",
              "      <td>Coca-Cola Bottling Company UNITED, Inc.</td>\n",
              "      <td>Coca-Cola Bottling Company UNITED was founded ...</td>\n",
              "      <td>6.0</td>\n",
              "      <td>AL - Alabama</td>\n",
              "      <td>US</td>\n",
              "      <td>Birmingham</td>\n",
              "      <td>35217</td>\n",
              "      <td>4600 East Lake Boulevard</td>\n",
              "      <td>https://www.linkedin.com/company/coca-cola-bot...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3903878594</td>\n",
              "      <td>Denver7 (KMGH-TV)</td>\n",
              "      <td>Mountain Multimedia Journalist, KMGH</td>\n",
              "      <td>KMGH, the E.W. Scripps Company ABC affiliate i...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Denver, CO</td>\n",
              "      <td>11500365.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>8031.0</td>\n",
              "      <td>Denver7 (KMGH-TV)</td>\n",
              "      <td>Founded in 1953 and acquired by the E.W. Scrip...</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Colorado</td>\n",
              "      <td>US</td>\n",
              "      <td>Denver</td>\n",
              "      <td>0</td>\n",
              "      <td>123 E Speer Blvd</td>\n",
              "      <td>https://www.linkedin.com/company/denver7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3905670593</td>\n",
              "      <td>BAYADA Home Health Care</td>\n",
              "      <td>Licensed Practical Nurse (LPN)</td>\n",
              "      <td>Come for the Flexibility, Stay for the Culture...</td>\n",
              "      <td>35.00</td>\n",
              "      <td>HOURLY</td>\n",
              "      <td>Teterboro, NJ</td>\n",
              "      <td>19472.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>34003.0</td>\n",
              "      <td>BAYADA Home Health Care</td>\n",
              "      <td>BAYADA Home Health Care was founded by J. Mark...</td>\n",
              "      <td>7.0</td>\n",
              "      <td>New Jersey</td>\n",
              "      <td>US</td>\n",
              "      <td>Pennsauken Township</td>\n",
              "      <td>0</td>\n",
              "      <td>4300 Haddonfield Rd</td>\n",
              "      <td>https://www.linkedin.com/company/bayada-home-h...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 40 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2beeaa05-bee9-4a1b-8397-71e7e6acebeb')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-2beeaa05-bee9-4a1b-8397-71e7e6acebeb button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-2beeaa05-bee9-4a1b-8397-71e7e6acebeb');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\nData types and non-null counts:\")\n",
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FFygi5HhUb4s",
        "outputId": "aaa77ad7-5cb2-4b16-b501-aedd3d3c0cce"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Data types and non-null counts:\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 30000 entries, 0 to 29999\n",
            "Data columns (total 40 columns):\n",
            " #   Column                      Non-Null Count  Dtype  \n",
            "---  ------                      --------------  -----  \n",
            " 0   job_id                      30000 non-null  int64  \n",
            " 1   company_name                29595 non-null  object \n",
            " 2   title                       30000 non-null  object \n",
            " 3   description                 29997 non-null  object \n",
            " 4   max_salary                  7204 non-null   float64\n",
            " 5   pay_period                  8666 non-null   object \n",
            " 6   location                    30000 non-null  object \n",
            " 7   company_id                  29595 non-null  float64\n",
            " 8   views                       29572 non-null  float64\n",
            " 9   med_salary                  1462 non-null   float64\n",
            " 10  min_salary                  7204 non-null   float64\n",
            " 11  formatted_work_type         30000 non-null  object \n",
            " 12  applies                     5675 non-null   float64\n",
            " 13  original_listed_time        30000 non-null  float64\n",
            " 14  remote_allowed              3663 non-null   float64\n",
            " 15  job_posting_url             30000 non-null  object \n",
            " 16  application_url             21249 non-null  object \n",
            " 17  application_type            30000 non-null  object \n",
            " 18  expiry                      30000 non-null  float64\n",
            " 19  closed_time                 251 non-null    float64\n",
            " 20  formatted_experience_level  22909 non-null  object \n",
            " 21  skills_desc                 583 non-null    object \n",
            " 22  listed_time                 30000 non-null  float64\n",
            " 23  posting_domain              20446 non-null  object \n",
            " 24  sponsored                   30000 non-null  int64  \n",
            " 25  work_type                   30000 non-null  object \n",
            " 26  currency                    8666 non-null   object \n",
            " 27  compensation_type           8666 non-null   object \n",
            " 28  normalized_salary           8666 non-null   float64\n",
            " 29  zip_code                    24955 non-null  float64\n",
            " 30  fips                        23405 non-null  float64\n",
            " 31  name                        29595 non-null  object \n",
            " 32  description_company         29315 non-null  object \n",
            " 33  company_size                28405 non-null  float64\n",
            " 34  state                       29582 non-null  object \n",
            " 35  country                     29595 non-null  object \n",
            " 36  city                        29595 non-null  object \n",
            " 37  zip_code_company            29532 non-null  object \n",
            " 38  address                     29581 non-null  object \n",
            " 39  url                         29595 non-null  object \n",
            "dtypes: float64(15), int64(2), object(23)\n",
            "memory usage: 9.2+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\nMissing values by column:\")\n",
        "missing_values = df.isnull().sum().sort_values(ascending=False)\n",
        "display(missing_values)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "OmUuld-OUfVO",
        "outputId": "687f2d7b-c715-4350-d7f4-1c378884a3ab"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Missing values by column:\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "closed_time                   29749\n",
              "skills_desc                   29417\n",
              "med_salary                    28538\n",
              "remote_allowed                26337\n",
              "applies                       24325\n",
              "max_salary                    22796\n",
              "min_salary                    22796\n",
              "pay_period                    21334\n",
              "normalized_salary             21334\n",
              "compensation_type             21334\n",
              "currency                      21334\n",
              "posting_domain                 9554\n",
              "application_url                8751\n",
              "formatted_experience_level     7091\n",
              "fips                           6595\n",
              "zip_code                       5045\n",
              "company_size                   1595\n",
              "description_company             685\n",
              "zip_code_company                468\n",
              "views                           428\n",
              "address                         419\n",
              "state                           418\n",
              "company_id                      405\n",
              "company_name                    405\n",
              "city                            405\n",
              "url                             405\n",
              "name                            405\n",
              "country                         405\n",
              "description                       3\n",
              "location                          0\n",
              "job_id                            0\n",
              "title                             0\n",
              "listed_time                       0\n",
              "expiry                            0\n",
              "application_type                  0\n",
              "job_posting_url                   0\n",
              "original_listed_time              0\n",
              "formatted_work_type               0\n",
              "work_type                         0\n",
              "sponsored                         0\n",
              "dtype: int64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>closed_time</th>\n",
              "      <td>29749</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>skills_desc</th>\n",
              "      <td>29417</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>med_salary</th>\n",
              "      <td>28538</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>remote_allowed</th>\n",
              "      <td>26337</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>applies</th>\n",
              "      <td>24325</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max_salary</th>\n",
              "      <td>22796</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min_salary</th>\n",
              "      <td>22796</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>pay_period</th>\n",
              "      <td>21334</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>normalized_salary</th>\n",
              "      <td>21334</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>compensation_type</th>\n",
              "      <td>21334</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>currency</th>\n",
              "      <td>21334</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>posting_domain</th>\n",
              "      <td>9554</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>application_url</th>\n",
              "      <td>8751</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>formatted_experience_level</th>\n",
              "      <td>7091</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fips</th>\n",
              "      <td>6595</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>zip_code</th>\n",
              "      <td>5045</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>company_size</th>\n",
              "      <td>1595</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>description_company</th>\n",
              "      <td>685</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>zip_code_company</th>\n",
              "      <td>468</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>views</th>\n",
              "      <td>428</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>address</th>\n",
              "      <td>419</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>state</th>\n",
              "      <td>418</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>company_id</th>\n",
              "      <td>405</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>company_name</th>\n",
              "      <td>405</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>city</th>\n",
              "      <td>405</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>url</th>\n",
              "      <td>405</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>name</th>\n",
              "      <td>405</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>country</th>\n",
              "      <td>405</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>description</th>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>location</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>job_id</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>title</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>listed_time</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>expiry</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>application_type</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>job_posting_url</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>original_listed_time</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>formatted_work_type</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>work_type</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sponsored</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\nMissing value percentages:\")\n",
        "missing_percent = (df.isnull().mean() * 100).sort_values(ascending=False)\n",
        "display(missing_percent.round(2))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "OT6yyIjdUiav",
        "outputId": "d624f957-2e77-4258-db4f-cdd450583a83"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Missing value percentages:\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "closed_time                   99.16\n",
              "skills_desc                   98.06\n",
              "med_salary                    95.13\n",
              "remote_allowed                87.79\n",
              "applies                       81.08\n",
              "max_salary                    75.99\n",
              "min_salary                    75.99\n",
              "pay_period                    71.11\n",
              "normalized_salary             71.11\n",
              "compensation_type             71.11\n",
              "currency                      71.11\n",
              "posting_domain                31.85\n",
              "application_url               29.17\n",
              "formatted_experience_level    23.64\n",
              "fips                          21.98\n",
              "zip_code                      16.82\n",
              "company_size                   5.32\n",
              "description_company            2.28\n",
              "zip_code_company               1.56\n",
              "views                          1.43\n",
              "address                        1.40\n",
              "state                          1.39\n",
              "company_id                     1.35\n",
              "company_name                   1.35\n",
              "city                           1.35\n",
              "url                            1.35\n",
              "name                           1.35\n",
              "country                        1.35\n",
              "description                    0.01\n",
              "location                       0.00\n",
              "job_id                         0.00\n",
              "title                          0.00\n",
              "listed_time                    0.00\n",
              "expiry                         0.00\n",
              "application_type               0.00\n",
              "job_posting_url                0.00\n",
              "original_listed_time           0.00\n",
              "formatted_work_type            0.00\n",
              "work_type                      0.00\n",
              "sponsored                      0.00\n",
              "dtype: float64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>closed_time</th>\n",
              "      <td>99.16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>skills_desc</th>\n",
              "      <td>98.06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>med_salary</th>\n",
              "      <td>95.13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>remote_allowed</th>\n",
              "      <td>87.79</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>applies</th>\n",
              "      <td>81.08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max_salary</th>\n",
              "      <td>75.99</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min_salary</th>\n",
              "      <td>75.99</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>pay_period</th>\n",
              "      <td>71.11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>normalized_salary</th>\n",
              "      <td>71.11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>compensation_type</th>\n",
              "      <td>71.11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>currency</th>\n",
              "      <td>71.11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>posting_domain</th>\n",
              "      <td>31.85</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>application_url</th>\n",
              "      <td>29.17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>formatted_experience_level</th>\n",
              "      <td>23.64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fips</th>\n",
              "      <td>21.98</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>zip_code</th>\n",
              "      <td>16.82</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>company_size</th>\n",
              "      <td>5.32</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>description_company</th>\n",
              "      <td>2.28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>zip_code_company</th>\n",
              "      <td>1.56</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>views</th>\n",
              "      <td>1.43</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>address</th>\n",
              "      <td>1.40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>state</th>\n",
              "      <td>1.39</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>company_id</th>\n",
              "      <td>1.35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>company_name</th>\n",
              "      <td>1.35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>city</th>\n",
              "      <td>1.35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>url</th>\n",
              "      <td>1.35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>name</th>\n",
              "      <td>1.35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>country</th>\n",
              "      <td>1.35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>description</th>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>location</th>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>job_id</th>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>title</th>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>listed_time</th>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>expiry</th>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>application_type</th>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>job_posting_url</th>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>original_listed_time</th>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>formatted_work_type</th>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>work_type</th>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sponsored</th>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> float64</label>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\nSummary statistics for numeric columns:\")\n",
        "display(df.describe())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 335
        },
        "id": "-T3HdvZ2Ulmb",
        "outputId": "c5a440bd-ede7-4e55-c294-970011bbd18c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Summary statistics for numeric columns:\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "             job_id    max_salary    company_id         views     med_salary  \\\n",
              "count  3.000000e+04  7.204000e+03  2.959500e+04  29572.000000    1462.000000   \n",
              "mean   3.897166e+09  8.627799e+04  1.222586e+07     14.934905   21595.032285   \n",
              "std    6.741631e+07  9.304721e+04  2.570294e+07     98.794448   49387.713771   \n",
              "min    9.217160e+05  7.800000e+00  1.009000e+03      1.000000       0.000000   \n",
              "25%    3.894585e+09  4.500000e+01  1.435200e+04      3.000000      18.500000   \n",
              "50%    3.901993e+09  8.000000e+04  2.230030e+05      4.000000      25.260000   \n",
              "75%    3.904704e+09  1.370000e+05  7.579168e+06      8.000000    2531.500000   \n",
              "max    3.906267e+09  1.500000e+06  1.034588e+08   9949.000000  550000.000000   \n",
              "\n",
              "          min_salary      applies  original_listed_time  remote_allowed  \\\n",
              "count    7204.000000  5675.000000          3.000000e+04          3663.0   \n",
              "mean    60645.825391    10.319119          1.713152e+12             1.0   \n",
              "std     60474.673836    28.815437          4.815288e+08             0.0   \n",
              "min         1.000000     1.000000          1.706054e+12             1.0   \n",
              "25%        35.000000     1.000000          1.712863e+12             1.0   \n",
              "50%     60000.000000     3.000000          1.713395e+12             1.0   \n",
              "75%     99406.000000     8.000000          1.713478e+12             1.0   \n",
              "max    750000.000000   729.000000          1.713573e+12             1.0   \n",
              "\n",
              "             expiry   closed_time   listed_time  sponsored  normalized_salary  \\\n",
              "count  3.000000e+04  2.510000e+02  3.000000e+04    30000.0       8.666000e+03   \n",
              "mean   1.716208e+12  1.712931e+12  1.713203e+12        0.0       3.137312e+05   \n",
              "std    2.316490e+09  3.697075e+08  3.993983e+08        0.0       8.106355e+06   \n",
              "min    1.712954e+12  1.712350e+12  1.712346e+12        0.0       0.000000e+00   \n",
              "25%    1.715481e+12  1.712670e+12  1.712886e+12        0.0       5.100000e+04   \n",
              "50%    1.716042e+12  1.712670e+12  1.713407e+12        0.0       8.008000e+04   \n",
              "75%    1.716088e+12  1.713285e+12  1.713484e+12        0.0       1.225000e+05   \n",
              "max    1.729125e+12  1.713557e+12  1.713573e+12        0.0       5.356000e+08   \n",
              "\n",
              "           zip_code          fips  company_size  \n",
              "count  24955.000000  23405.000000  28405.000000  \n",
              "mean   50261.727990  28649.052510      4.934624  \n",
              "std    30192.064914  15995.812341      2.057626  \n",
              "min     1001.000000   1003.000000      1.000000  \n",
              "25%    24230.500000  13115.000000      3.000000  \n",
              "50%    47630.000000  29183.000000      5.000000  \n",
              "75%    78201.000000  42071.000000      7.000000  \n",
              "max    99901.000000  56043.000000      7.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c4344aa9-40ad-4275-be9d-24a8da9d954a\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>job_id</th>\n",
              "      <th>max_salary</th>\n",
              "      <th>company_id</th>\n",
              "      <th>views</th>\n",
              "      <th>med_salary</th>\n",
              "      <th>min_salary</th>\n",
              "      <th>applies</th>\n",
              "      <th>original_listed_time</th>\n",
              "      <th>remote_allowed</th>\n",
              "      <th>expiry</th>\n",
              "      <th>closed_time</th>\n",
              "      <th>listed_time</th>\n",
              "      <th>sponsored</th>\n",
              "      <th>normalized_salary</th>\n",
              "      <th>zip_code</th>\n",
              "      <th>fips</th>\n",
              "      <th>company_size</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>3.000000e+04</td>\n",
              "      <td>7.204000e+03</td>\n",
              "      <td>2.959500e+04</td>\n",
              "      <td>29572.000000</td>\n",
              "      <td>1462.000000</td>\n",
              "      <td>7204.000000</td>\n",
              "      <td>5675.000000</td>\n",
              "      <td>3.000000e+04</td>\n",
              "      <td>3663.0</td>\n",
              "      <td>3.000000e+04</td>\n",
              "      <td>2.510000e+02</td>\n",
              "      <td>3.000000e+04</td>\n",
              "      <td>30000.0</td>\n",
              "      <td>8.666000e+03</td>\n",
              "      <td>24955.000000</td>\n",
              "      <td>23405.000000</td>\n",
              "      <td>28405.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>3.897166e+09</td>\n",
              "      <td>8.627799e+04</td>\n",
              "      <td>1.222586e+07</td>\n",
              "      <td>14.934905</td>\n",
              "      <td>21595.032285</td>\n",
              "      <td>60645.825391</td>\n",
              "      <td>10.319119</td>\n",
              "      <td>1.713152e+12</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.716208e+12</td>\n",
              "      <td>1.712931e+12</td>\n",
              "      <td>1.713203e+12</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.137312e+05</td>\n",
              "      <td>50261.727990</td>\n",
              "      <td>28649.052510</td>\n",
              "      <td>4.934624</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>6.741631e+07</td>\n",
              "      <td>9.304721e+04</td>\n",
              "      <td>2.570294e+07</td>\n",
              "      <td>98.794448</td>\n",
              "      <td>49387.713771</td>\n",
              "      <td>60474.673836</td>\n",
              "      <td>28.815437</td>\n",
              "      <td>4.815288e+08</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.316490e+09</td>\n",
              "      <td>3.697075e+08</td>\n",
              "      <td>3.993983e+08</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8.106355e+06</td>\n",
              "      <td>30192.064914</td>\n",
              "      <td>15995.812341</td>\n",
              "      <td>2.057626</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>9.217160e+05</td>\n",
              "      <td>7.800000e+00</td>\n",
              "      <td>1.009000e+03</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.706054e+12</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.712954e+12</td>\n",
              "      <td>1.712350e+12</td>\n",
              "      <td>1.712346e+12</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>1001.000000</td>\n",
              "      <td>1003.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>3.894585e+09</td>\n",
              "      <td>4.500000e+01</td>\n",
              "      <td>1.435200e+04</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>18.500000</td>\n",
              "      <td>35.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.712863e+12</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.715481e+12</td>\n",
              "      <td>1.712670e+12</td>\n",
              "      <td>1.712886e+12</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5.100000e+04</td>\n",
              "      <td>24230.500000</td>\n",
              "      <td>13115.000000</td>\n",
              "      <td>3.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>3.901993e+09</td>\n",
              "      <td>8.000000e+04</td>\n",
              "      <td>2.230030e+05</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>25.260000</td>\n",
              "      <td>60000.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.713395e+12</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.716042e+12</td>\n",
              "      <td>1.712670e+12</td>\n",
              "      <td>1.713407e+12</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8.008000e+04</td>\n",
              "      <td>47630.000000</td>\n",
              "      <td>29183.000000</td>\n",
              "      <td>5.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>3.904704e+09</td>\n",
              "      <td>1.370000e+05</td>\n",
              "      <td>7.579168e+06</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>2531.500000</td>\n",
              "      <td>99406.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>1.713478e+12</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.716088e+12</td>\n",
              "      <td>1.713285e+12</td>\n",
              "      <td>1.713484e+12</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.225000e+05</td>\n",
              "      <td>78201.000000</td>\n",
              "      <td>42071.000000</td>\n",
              "      <td>7.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>3.906267e+09</td>\n",
              "      <td>1.500000e+06</td>\n",
              "      <td>1.034588e+08</td>\n",
              "      <td>9949.000000</td>\n",
              "      <td>550000.000000</td>\n",
              "      <td>750000.000000</td>\n",
              "      <td>729.000000</td>\n",
              "      <td>1.713573e+12</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.729125e+12</td>\n",
              "      <td>1.713557e+12</td>\n",
              "      <td>1.713573e+12</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5.356000e+08</td>\n",
              "      <td>99901.000000</td>\n",
              "      <td>56043.000000</td>\n",
              "      <td>7.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c4344aa9-40ad-4275-be9d-24a8da9d954a')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-c4344aa9-40ad-4275-be9d-24a8da9d954a button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-c4344aa9-40ad-4275-be9d-24a8da9d954a');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"display(df\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"job_id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2007245034.466158,\n        \"min\": 30000.0,\n        \"max\": 3906267195.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          3897165990.2674665,\n          3901993166.5,\n          30000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"max_salary\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 512468.7476590226,\n        \"min\": 7.8,\n        \"max\": 1500000.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          86277.98747779013,\n          80000.0,\n          7204.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"company_id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 35430925.872851044,\n        \"min\": 1009.0,\n        \"max\": 103458790.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          12225857.289136678,\n          223003.0,\n          29595.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"views\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10535.472842618567,\n        \"min\": 1.0,\n        \"max\": 29572.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          14.934904639523873,\n          4.0,\n          29572.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"med_salary\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 191454.13013164987,\n        \"min\": 0.0,\n        \"max\": 550000.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          21595.032284541725,\n          25.259999999999998,\n          1462.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"min_salary\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 253193.5004317354,\n        \"min\": 1.0,\n        \"max\": 750000.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          60645.825391449194,\n          60000.0,\n          7204.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"applies\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1983.066479616194,\n        \"min\": 1.0,\n        \"max\": 5675.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          5675.0,\n          10.319118942731277,\n          8.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"original_listed_time\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 792434292324.9916,\n        \"min\": 30000.0,\n        \"max\": 1713572687000.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          1713152226642.0667,\n          1713394810000.0,\n          30000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"remote_allowed\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1294.7630712660466,\n        \"min\": 0.0,\n        \"max\": 3663.0,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          3663.0,\n          1.0,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"expiry\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 794596138038.1833,\n        \"min\": 30000.0,\n        \"max\": 1729124518000.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          1716208342869.2666,\n          1716041762500.0,\n          30000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"closed_time\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 792837910536.6572,\n        \"min\": 251.0,\n        \"max\": 1713556566000.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          1712930856386.454,\n          1712669893000.0,\n          251.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"listed_time\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 792941893600.6552,\n        \"min\": 30000.0,\n        \"max\": 1713572816000.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          1713203468219.6,\n          1713407146000.0,\n          30000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sponsored\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10606.601717798212,\n        \"min\": 0.0,\n        \"max\": 30000.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.0,\n          30000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"normalized_salary\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 188945490.39094657,\n        \"min\": 0.0,\n        \"max\": 535600000.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          313731.16690918535,\n          80080.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"zip_code\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 31932.39499059705,\n        \"min\": 1001.0,\n        \"max\": 99901.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          50261.727990382686,\n          47630.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"fips\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 17218.887716233523,\n        \"min\": 1003.0,\n        \"max\": 56043.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          28649.052510147405,\n          29183.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"company_size\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10041.16945770525,\n        \"min\": 1.0,\n        \"max\": 28405.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          28405.0,\n          4.934624185882767\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The preliminary overview shows that the dataset contains a mix of numeric, categorical, and text-based columns. Several columns contain missing values, especially salary-related and company-related fields.  The next preprocessing steps  focus on handling missing values, creating salary transparency indicators, and preparing the data for EDA and modeling."
      ],
      "metadata": {
        "id": "apHCuEWIVHNN"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Define the Regression Target\n",
        "The main target variable for this project is `views`, which represents the number of times a job posting was viewed.\n",
        "\n",
        "This variable is used as a measure of job-posting engagement. Before modeling, I inspect its distribution and summary statistics to understand its scale, skewness, and potential outliers."
      ],
      "metadata": {
        "id": "WtQMdsGAXLI8"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 491
        },
        "id": "MrfQ-hesYnmk",
        "outputId": "4beb3a96-2dbd-4fe3-c584-8aab8a76d21d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Target column: views\n",
            "\n",
            "Summary statistics for views:\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "count    29572.000000\n",
              "mean        14.934905\n",
              "std         98.794448\n",
              "min          1.000000\n",
              "25%          3.000000\n",
              "50%          4.000000\n",
              "75%          8.000000\n",
              "max       9949.000000\n",
              "Name: views, dtype: float64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>views</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>29572.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>14.934905</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>98.794448</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>3.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>4.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>8.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>9949.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> float64</label>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Missing values in target:\n",
            "428\n",
            "\n",
            "Number of zero or negative views:\n",
            "0\n"
          ]
        }
      ],
      "source": [
        "# 1.3 Define Regression Target: Job Views\n",
        "\n",
        "target_col = \"views\"\n",
        "\n",
        "if target_col not in df.columns:\n",
        "    raise ValueError(\"The target column 'views' was not found. Check the dataset columns.\")\n",
        "\n",
        "print(\"Target column:\", target_col)\n",
        "\n",
        "print(\"\\nSummary statistics for views:\")\n",
        "display(df[target_col].describe())\n",
        "\n",
        "print(\"\\nMissing values in target:\")\n",
        "print(df[target_col].isna().sum())\n",
        "\n",
        "print(\"\\nNumber of zero or negative views:\")\n",
        "print((df[target_col] <= 0).sum())"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The `views` column is used as the main engagement target. Since view counts are usually highly skewed, the raw target is inspected before modeling. This motivates using a log-transformed target later in the regression workflow."
      ],
      "metadata": {
        "id": "6sC_uPNwXaPG"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6eLmNWJJIPS0"
      },
      "source": [
        "# **Part 2: Exploratory Data Analysis (EDA)**\n",
        "\n",
        "Use your EDA to tell the story of your data - highlight interesting patterns, anomalies, or relationships that lead you toward your classification goal. Ask interesting questions, and answer them.\n",
        "\n",
        "\n",
        "1. **Data Cleaning** : Check for missing values, duplicate entries, scaling/normalize issues,  parsing dates, fixing typos, or any inconsistencies.  Document how you address them.\n",
        "2. **Outlier Detection & Handling**: Identify outliers and decide whether to keep or remove them, providing a short justification.\n",
        "2. **Descriptive Statistics**: Summarize the data (e.g., mean, median, correlations) to reveal patterns.\n",
        "4. **Visualizations**: Use a set of  plots (e.g., histograms, scatter plots, box plots) to illustrate **key insights.** Label charts, axes, and legends clearly.\n",
        "\n",
        "Tip: not necessarily in this order."
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##  Basic Cleaning\n",
        "\n",
        "Before deeper EDA and feature engineering, I perform basic cleaning steps:\n",
        "\n",
        "- Remove duplicate rows.\n",
        "- Remove rows where the target variable `views` is missing.\n",
        "- Keep only rows with valid non-negative view counts.\n",
        "\n",
        "This ensures that the regression target is usable before creating the analysis dataset."
      ],
      "metadata": {
        "id": "vAZlT6AsYGnO"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Qe5k-9DLK07M",
        "outputId": "5ef0d294-e49b-412e-a6b5-092688386571"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Shape after basic cleaning: (29572, 40)\n",
            "Duplicate rows after cleaning: 0\n",
            "Missing target values after cleaning: 0\n",
            "Negative target values after cleaning: 0\n"
          ]
        }
      ],
      "source": [
        "# 2.1 Basic Data Cleaning\n",
        "\n",
        "# Remove duplicate rows\n",
        "df = df.drop_duplicates().reset_index(drop=True)\n",
        "\n",
        "# Remove rows without target\n",
        "df = df.dropna(subset=[target_col])\n",
        "\n",
        "# Keep only valid non-negative target values\n",
        "df = df[df[target_col] >= 0].copy()\n",
        "\n",
        "print(\"\\nShape after basic cleaning:\", df.shape)\n",
        "print(\"Duplicate rows after cleaning:\", df.duplicated().sum())\n",
        "print(\"Missing target values after cleaning:\", df[target_col].isna().sum())\n",
        "print(\"Negative target values after cleaning:\", (df[target_col] < 0).sum())"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##  Date Parsing and Time-Based Features\n",
        "\n",
        "Several job-posting columns contain time information, such as when a job was listed or when it expired. These columns are parsed into datetime format so that useful time-based features can be created.\n",
        "\n",
        "The main engineered time features are:\n",
        "\n",
        "| Feature | Meaning |\n",
        "|---|---|\n",
        "| `posting_year` | Year the job was posted |\n",
        "| `posting_month` | Month the job was posted |\n",
        "| `posting_dayofweek` | Day of week the job was posted |\n",
        "| `posting_weekend` | Whether the job was posted on Saturday or Sunday |\n",
        "\n",
        "These features allow the models to test whether posting timing is associated with engagement."
      ],
      "metadata": {
        "id": "rxUXdSCEYtq7"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pK9-QwmDYxT8",
        "outputId": "b354f665-a474-4034-afa4-d30d141acaf3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Parsed date column: listed_time\n",
            "Parsed date column: original_listed_time\n",
            "Parsed date column: expiry\n",
            "Parsed date column: closed_time\n",
            "\n",
            "Date column used for posting features: listed_time\n",
            "Created features: posting_year, posting_month, posting_dayofweek, posting_weekend\n"
          ]
        }
      ],
      "source": [
        "# 2.2 Date Parsing and Time-Based Features\n",
        "\n",
        "possible_date_cols = [\n",
        "    \"listed_time\",\n",
        "    \"original_listed_time\",\n",
        "    \"expiry\",\n",
        "    \"closed_time\",\n",
        "    \"listed_at\",\n",
        "    \"created_at\"\n",
        "]\n",
        "\n",
        "parsed_date_cols = []\n",
        "\n",
        "for col in possible_date_cols:\n",
        "    if col in df.columns:\n",
        "        # If the column is numeric, treat it as a millisecond timestamp.\n",
        "        if pd.api.types.is_numeric_dtype(df[col]):\n",
        "            df[col] = pd.to_datetime(df[col], errors=\"coerce\", unit=\"ms\")\n",
        "        else:\n",
        "            df[col] = pd.to_datetime(df[col], errors=\"coerce\")\n",
        "\n",
        "        parsed_date_cols.append(col)\n",
        "        print(f\"Parsed date column: {col}\")\n",
        "\n",
        "# Use the best available posting date column\n",
        "date_col = None\n",
        "\n",
        "for col in [\"listed_time\", \"original_listed_time\", \"listed_at\", \"created_at\"]:\n",
        "    if col in df.columns and df[col].notna().sum() > 0:\n",
        "        date_col = col\n",
        "        break\n",
        "\n",
        "if date_col is not None:\n",
        "    df[\"posting_year\"] = df[date_col].dt.year\n",
        "    df[\"posting_month\"] = df[date_col].dt.month\n",
        "    df[\"posting_dayofweek\"] = df[date_col].dt.dayofweek\n",
        "    df[\"posting_weekend\"] = df[\"posting_dayofweek\"].isin([5, 6]).astype(int)\n",
        "\n",
        "    print(\"\\nDate column used for posting features:\", date_col)\n",
        "    print(\"Created features: posting_year, posting_month, posting_dayofweek, posting_weekend\")\n",
        "else:\n",
        "    print(\"\\nNo usable posting date column found.\")"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Missing Value Summary\n",
        "\n",
        "After basic cleaning and date parsing, I summarize missing values across all columns. This helps decide which variables can be used directly, which need imputation, and which may be too incomplete to keep.\n",
        "Columns with more than 70% missing values were removed, except for important fields needed for target definition, EDA, or feature engineering."
      ],
      "metadata": {
        "id": "QoRYfa1QZo3-"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 990
        },
        "id": "O_Zr8aqeY1d6",
        "outputId": "7d61adba-1f4c-4c5a-a2af-6cfc02701028"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                            missing_count  missing_percent\n",
              "closed_time                         29321            99.15\n",
              "skills_desc                         29011            98.10\n",
              "med_salary                          28126            95.11\n",
              "remote_allowed                      25993            87.90\n",
              "applies                             23897            80.81\n",
              "max_salary                          22456            75.94\n",
              "min_salary                          22456            75.94\n",
              "compensation_type                   21010            71.05\n",
              "currency                            21010            71.05\n",
              "normalized_salary                   21010            71.05\n",
              "pay_period                          21010            71.05\n",
              "posting_domain                       9245            31.26\n",
              "application_url                      8499            28.74\n",
              "formatted_experience_level           6969            23.57\n",
              "fips                                 6479            21.91\n",
              "zip_code                             4951            16.74\n",
              "company_size                         1547             5.23\n",
              "description_company                   662             2.24\n",
              "zip_code_company                      455             1.54\n",
              "address                               406             1.37\n",
              "state                                 405             1.37\n",
              "country                               392             1.33\n",
              "city                                  392             1.33\n",
              "name                                  392             1.33\n",
              "url                                   392             1.33\n",
              "company_id                            392             1.33\n",
              "company_name                          392             1.33\n",
              "description                             3             0.01\n",
              "formatted_work_type                     0             0.00\n",
              "views                                   0             0.00"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-995ee7fa-8282-4073-b06f-0ecece37732d\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>missing_count</th>\n",
              "      <th>missing_percent</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>closed_time</th>\n",
              "      <td>29321</td>\n",
              "      <td>99.15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>skills_desc</th>\n",
              "      <td>29011</td>\n",
              "      <td>98.10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>med_salary</th>\n",
              "      <td>28126</td>\n",
              "      <td>95.11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>remote_allowed</th>\n",
              "      <td>25993</td>\n",
              "      <td>87.90</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>applies</th>\n",
              "      <td>23897</td>\n",
              "      <td>80.81</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max_salary</th>\n",
              "      <td>22456</td>\n",
              "      <td>75.94</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min_salary</th>\n",
              "      <td>22456</td>\n",
              "      <td>75.94</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>compensation_type</th>\n",
              "      <td>21010</td>\n",
              "      <td>71.05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>currency</th>\n",
              "      <td>21010</td>\n",
              "      <td>71.05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>normalized_salary</th>\n",
              "      <td>21010</td>\n",
              "      <td>71.05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>pay_period</th>\n",
              "      <td>21010</td>\n",
              "      <td>71.05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>posting_domain</th>\n",
              "      <td>9245</td>\n",
              "      <td>31.26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>application_url</th>\n",
              "      <td>8499</td>\n",
              "      <td>28.74</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>formatted_experience_level</th>\n",
              "      <td>6969</td>\n",
              "      <td>23.57</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fips</th>\n",
              "      <td>6479</td>\n",
              "      <td>21.91</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>zip_code</th>\n",
              "      <td>4951</td>\n",
              "      <td>16.74</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>company_size</th>\n",
              "      <td>1547</td>\n",
              "      <td>5.23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>description_company</th>\n",
              "      <td>662</td>\n",
              "      <td>2.24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>zip_code_company</th>\n",
              "      <td>455</td>\n",
              "      <td>1.54</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>address</th>\n",
              "      <td>406</td>\n",
              "      <td>1.37</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>state</th>\n",
              "      <td>405</td>\n",
              "      <td>1.37</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>country</th>\n",
              "      <td>392</td>\n",
              "      <td>1.33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>city</th>\n",
              "      <td>392</td>\n",
              "      <td>1.33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>name</th>\n",
              "      <td>392</td>\n",
              "      <td>1.33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>url</th>\n",
              "      <td>392</td>\n",
              "      <td>1.33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>company_id</th>\n",
              "      <td>392</td>\n",
              "      <td>1.33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>company_name</th>\n",
              "      <td>392</td>\n",
              "      <td>1.33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>description</th>\n",
              "      <td>3</td>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>formatted_work_type</th>\n",
              "      <td>0</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>views</th>\n",
              "      <td>0</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-995ee7fa-8282-4073-b06f-0ecece37732d')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-995ee7fa-8282-4073-b06f-0ecece37732d button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-995ee7fa-8282-4073-b06f-0ecece37732d');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"display(missing_summary\",\n  \"rows\": 30,\n  \"fields\": [\n    {\n      \"column\": \"missing_count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11208,\n        \"min\": 0,\n        \"max\": 29321,\n        \"num_unique_values\": 20,\n        \"samples\": [\n          29321,\n          392,\n          406\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"missing_percent\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 37.90166042673392,\n        \"min\": 0.0,\n        \"max\": 99.15,\n        \"num_unique_values\": 19,\n        \"samples\": [\n          99.15,\n          75.94,\n          16.74\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "# 2.3 Missing Value Analysis\n",
        "\n",
        "missing_summary = pd.DataFrame({\n",
        "    \"missing_count\": df.isnull().sum(),\n",
        "    \"missing_percent\": df.isnull().mean() * 100\n",
        "}).sort_values(\"missing_percent\", ascending=False)\n",
        "\n",
        "display(missing_summary.head(30).round(2))"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##  Dropping Columns\n",
        "Columns with very high missingness are unlikely to support reliable modeling because most rows do not contain usable information. I use a 70% missing-value threshold to identify these columns.\n",
        "\n",
        "However, some important columns are protected from removal even if they have missing values. For example, missing salary information will can be transformed into a useful feature."
      ],
      "metadata": {
        "id": "_iBnhtp1aG_U"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "edJLp-2mZBps",
        "outputId": "e97703c4-b64e-4c06-c196-f9897f1622fd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "High-missing columns identified: ['closed_time', 'skills_desc', 'med_salary', 'remote_allowed', 'applies', 'max_salary', 'min_salary', 'compensation_type', 'currency', 'normalized_salary', 'pay_period']\n",
            "\n",
            "Protected columns kept: ['views', 'views', 'title', 'description', 'min_salary', 'max_salary', 'med_salary', 'pay_period', 'formatted_work_type', 'formatted_experience_level', 'company_size', 'company_id']\n",
            "\n",
            "Dropping high-missing columns: ['closed_time', 'skills_desc', 'remote_allowed', 'applies', 'compensation_type', 'currency', 'normalized_salary']\n",
            "\n",
            "Shape after dropping high-missing columns: (29572, 37)\n"
          ]
        }
      ],
      "source": [
        "# Protect important columns and drop the other columns that cross high missing threshold\n",
        "high_missing_threshold = 70\n",
        "\n",
        "high_missing_cols = missing_summary[\n",
        "    missing_summary[\"missing_percent\"] > high_missing_threshold\n",
        "].index.tolist()\n",
        "\n",
        "# Protect important columns that are useful for target definition, EDA, or feature engineering\n",
        "protected_cols = [\n",
        "    target_col,\n",
        "    \"views\",\n",
        "    \"title\",\n",
        "    \"description\",\n",
        "    \"min_salary\",\n",
        "    \"max_salary\",\n",
        "    \"med_salary\",\n",
        "    \"pay_period\",\n",
        "    \"formatted_work_type\",\n",
        "    \"formatted_experience_level\",\n",
        "    \"company_size\",\n",
        "    \"company_id\"\n",
        "]\n",
        "\n",
        "protected_cols = [col for col in protected_cols if col in df.columns]\n",
        "\n",
        "high_missing_cols_to_drop = [\n",
        "    col for col in high_missing_cols\n",
        "    if col not in protected_cols\n",
        "]\n",
        "\n",
        "print(\"High-missing columns identified:\", high_missing_cols)\n",
        "print(\"\\nProtected columns kept:\", protected_cols)\n",
        "print(\"\\nDropping high-missing columns:\", high_missing_cols_to_drop)\n",
        "\n",
        "df = df.drop(columns=high_missing_cols_to_drop, errors=\"ignore\")\n",
        "\n",
        "print(\"\\nShape after dropping high-missing columns:\", df.shape)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Missing-Value Handling Strategy\n",
        "\n",
        "The missing-value summary guides how different types of columns are handled before modeling.\n",
        "\n",
        "- **Columns with more than 70% missing values** are removed, unless they are important for EDA or feature engineering. These columns contain too few observations to be reliable predictors.\n",
        "\n",
        "- **Text columns**, such as job title and description, are not directly imputed. Instead, they are transformed into numeric features such as word count, character length, and description density. Missing text is treated as blank text when creating these features.\n",
        "\n",
        "- **Salary columns** are not dropped, even though many salary values are missing. Missing salary information may be meaningful, so I create a `has_salary_info` indicator and engineer salary-related features such as `salary_midpoint` and `salary_log`.\n",
        "\n",
        "- **Categorical columns** are handled through encoding and imputation when preparing the modeling pipeline.\n",
        "\n",
        "- **Remaining numeric missing values** are handled inside the sklearn pipeline using `SimpleImputer(strategy=\"median\")`. This helps avoid data leakage because the imputer is fit only on the training data.\n"
      ],
      "metadata": {
        "id": "LxGYmFOcO4SN"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Identify Potential Leakage Columns\n",
        "\n",
        "Before modeling, I identify columns that should not be used as predictors because they may leak information about job performance after the posting was published.\n",
        "\n",
        "For example, `applies` is likely an outcome that happens after users view or interact with a job posting. Since the goal is to predict engagement from posting-level features, post-performance variables should be excluded from the model features."
      ],
      "metadata": {
        "id": "nuOqgrP_c687"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JlNhs08CZGH0",
        "outputId": "711cbb07-3ec7-415e-b9db-57a46a627094"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Target column kept separately: views\n",
            "Potential leakage columns to exclude from features later:\n",
            "['expiry']\n"
          ]
        }
      ],
      "source": [
        "# 2.4 Identify Potential Leakage Columns\n",
        "\n",
        "leakage_cols = [\n",
        "    \"views\",        # target variable\n",
        "    \"applies\",      # post-outcome behavior, likely unavailable at posting creation\n",
        "    \"closed_time\",  # information from after posting lifecycle\n",
        "    \"expiry\"        # posting lifecycle field, not a core posting characteristic\n",
        "]\n",
        "\n",
        "existing_leakage_cols = [\n",
        "    col for col in leakage_cols\n",
        "    if col in df.columns\n",
        "]\n",
        "\n",
        "feature_leakage_cols = [\n",
        "    col for col in existing_leakage_cols\n",
        "    if col != target_col\n",
        "]\n",
        "\n",
        "print(\"Target column kept separately:\", target_col)\n",
        "print(\"Potential leakage columns to exclude from features later:\")\n",
        "print(feature_leakage_cols)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##  Feature Engineering\n",
        "\n",
        "After basic cleaning, I create additional features from the raw job-posting fields. These engineered features are designed to make the dataset more useful for EDA and modeling.\n",
        "\n",
        "The new features include text-length measures, salary transparency indicators, salary scale variables, role keyword flags, timing interactions, and salary-description interaction terms.\n",
        "\n",
        "Missing text is treated as blank text when creating text-length features. Missing numeric values, especially salary values, are left as missing where appropriate and later handled inside the sklearn modeling pipeline to reduce leakage risk.\n",
        "\n",
        "The engineered features make the raw job-posting data more informative. Instead of using raw text fields directly, the project extracts interpretable numeric signals such as word counts, salary transparency, seniority indicators, role categories, and interaction terms. These features are later used in EDA, clustering, regression, and classification."
      ],
      "metadata": {
        "id": "tGL2KcF6ejAZ"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZgoMJt-IZRB2",
        "outputId": "72b33562-db98-44b3-8ae1-c8a4ced80fb7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Feature engineering complete.\n",
            "Number of engineered features created: 21\n",
            "['title_length', 'title_word_count', 'description_length', 'description_word_count', 'has_salary_info', 'salary_midpoint', 'salary_range', 'salary_log', 'is_remote_text', 'is_senior_role', 'is_entry_role', 'is_software_role', 'is_data_role', 'is_manager_role', 'is_sales_role', 'is_marketing_role', 'description_density', 'title_desc_ratio', 'desc_salary_interaction', 'senior_salary', 'weekend_remote']\n"
          ]
        }
      ],
      "source": [
        "# 2.5 Create Text, Salary, Timing, and Role Features\n",
        "\n",
        "df_analysis = df.copy()\n",
        "\n",
        "created_features = []\n",
        "\n",
        "# Text features\n",
        "for col in [\"title\", \"description\", \"skills_desc\"]:\n",
        "    if col in df_analysis.columns:\n",
        "        df_analysis[f\"{col}_length\"] = (\n",
        "            df_analysis[col]\n",
        "            .fillna(\"\")\n",
        "            .astype(str)\n",
        "            .str.len()\n",
        "        )\n",
        "\n",
        "        df_analysis[f\"{col}_word_count\"] = (\n",
        "            df_analysis[col]\n",
        "            .fillna(\"\")\n",
        "            .astype(str)\n",
        "            .str.split()\n",
        "            .str.len()\n",
        "        )\n",
        "\n",
        "        created_features.extend([\n",
        "            f\"{col}_length\",\n",
        "            f\"{col}_word_count\"\n",
        "        ])\n",
        "\n",
        "# Salary features\n",
        "salary_cols = [\n",
        "    col for col in [\"min_salary\", \"max_salary\", \"med_salary\"]\n",
        "    if col in df_analysis.columns\n",
        "]\n",
        "\n",
        "if salary_cols:\n",
        "    df_analysis[\"has_salary_info\"] = (\n",
        "        df_analysis[salary_cols]\n",
        "        .notna()\n",
        "        .any(axis=1)\n",
        "        .astype(int)\n",
        "    )\n",
        "\n",
        "    created_features.append(\"has_salary_info\")\n",
        "\n",
        "if \"min_salary\" in df_analysis.columns and \"max_salary\" in df_analysis.columns:\n",
        "    df_analysis[\"salary_midpoint\"] = (\n",
        "        df_analysis[[\"min_salary\", \"max_salary\"]]\n",
        "        .mean(axis=1)\n",
        "    )\n",
        "\n",
        "    df_analysis[\"salary_range\"] = (\n",
        "        df_analysis[\"max_salary\"] - df_analysis[\"min_salary\"]\n",
        "    )\n",
        "\n",
        "    # Leave missing values as NaN.\n",
        "    # Missing salary values will be handled later by the sklearn pipeline.\n",
        "    df_analysis[\"salary_log\"] = np.log1p(df_analysis[\"salary_midpoint\"])\n",
        "\n",
        "    created_features.extend([\n",
        "        \"salary_midpoint\",\n",
        "        \"salary_range\",\n",
        "        \"salary_log\"\n",
        "    ])\n",
        "# Industry handling\n",
        "if \"industry\" in df_analysis.columns:\n",
        "    df_analysis[\"industry\"] = df_analysis[\"industry\"].fillna(\"Unknown\")\n",
        "# Remote feature from location text\n",
        "if \"location\" in df_analysis.columns:\n",
        "    df_analysis[\"is_remote_text\"] = (\n",
        "        df_analysis[\"location\"]\n",
        "        .fillna(\"\")\n",
        "        .astype(str)\n",
        "        .str.lower()\n",
        "        .str.contains(\"remote\", regex=False)\n",
        "        .astype(int)\n",
        "    )\n",
        "\n",
        "    created_features.append(\"is_remote_text\")\n",
        "# Role keyword features from job title\n",
        "if \"title\" in df_analysis.columns:\n",
        "    title_lower = (\n",
        "        df_analysis[\"title\"]\n",
        "        .fillna(\"\")\n",
        "        .astype(str)\n",
        "        .str.lower()\n",
        "    )\n",
        "\n",
        "    df_analysis[\"is_senior_role\"] = title_lower.str.contains(\n",
        "        r\"\\bsenior\\b|\\bsr\\.?\\b|\\blead\\b|\\bprincipal\\b|\\bstaff\\b\",\n",
        "        regex=True\n",
        "    ).astype(int)\n",
        "\n",
        "    df_analysis[\"is_entry_role\"] = title_lower.str.contains(\n",
        "        r\"\\bjunior\\b|\\bentry\\b|\\bintern\\b|\\binternship\\b|\\bgraduate\\b|\\btrainee\\b\",\n",
        "        regex=True\n",
        "    ).astype(int)\n",
        "\n",
        "    df_analysis[\"is_software_role\"] = title_lower.str.contains(\n",
        "        r\"\\bsoftware\\b|\\bdeveloper\\b|\\bengineer\\b|\\bprogrammer\\b|\\bbackend\\b|\\bfrontend\\b|\\bfullstack\\b|\\bfull stack\\b\",\n",
        "        regex=True\n",
        "    ).astype(int)\n",
        "\n",
        "    df_analysis[\"is_data_role\"] = title_lower.str.contains(\n",
        "        r\"\\bdata\\b|\\banalyst\\b|\\banalytics\\b|\\bscientist\\b|\\bmachine learning\\b|\\bml\\b|\\bai\\b\",\n",
        "        regex=True\n",
        "    ).astype(int)\n",
        "\n",
        "    df_analysis[\"is_manager_role\"] = title_lower.str.contains(\n",
        "        r\"\\bmanager\\b|\\bmanagement\\b|\\blead\\b|\\bhead\\b|\\bdirector\\b|\\bvp\\b|\\bchief\\b\",\n",
        "        regex=True\n",
        "    ).astype(int)\n",
        "\n",
        "    df_analysis[\"is_sales_role\"] = title_lower.str.contains(\n",
        "        r\"\\bsales\\b|\\baccount executive\\b|\\bbusiness development\\b|\\bbd\\b\",\n",
        "        regex=True\n",
        "    ).astype(int)\n",
        "\n",
        "    df_analysis[\"is_marketing_role\"] = title_lower.str.contains(\n",
        "        r\"\\bmarketing\\b|\\bbrand\\b|\\bgrowth\\b|\\bcontent\\b|\\bseo\\b|\\bsocial media\\b\",\n",
        "        regex=True\n",
        "    ).astype(int)\n",
        "\n",
        "    created_features.extend([\n",
        "        \"is_senior_role\",\n",
        "        \"is_entry_role\",\n",
        "        \"is_software_role\",\n",
        "        \"is_data_role\",\n",
        "        \"is_manager_role\",\n",
        "        \"is_sales_role\",\n",
        "        \"is_marketing_role\"\n",
        "    ])\n",
        "\n",
        "# Description density and ratios\n",
        "if \"description_length\" in df_analysis.columns and \"description_word_count\" in df_analysis.columns:\n",
        "    df_analysis[\"description_density\"] = (\n",
        "        df_analysis[\"description_length\"] /\n",
        "        (df_analysis[\"description_word_count\"] + 1)\n",
        "    )\n",
        "\n",
        "    created_features.append(\"description_density\")\n",
        "\n",
        "if \"title_word_count\" in df_analysis.columns and \"description_word_count\" in df_analysis.columns:\n",
        "    df_analysis[\"title_desc_ratio\"] = (\n",
        "        df_analysis[\"title_word_count\"] /\n",
        "        (df_analysis[\"description_word_count\"] + 1)\n",
        "    )\n",
        "\n",
        "    created_features.append(\"title_desc_ratio\")\n",
        "\n",
        "# Interaction features\n",
        "if \"description_word_count\" in df_analysis.columns and \"salary_midpoint\" in df_analysis.columns:\n",
        "    df_analysis[\"desc_salary_interaction\"] = (\n",
        "        df_analysis[\"description_word_count\"] *\n",
        "        df_analysis[\"salary_midpoint\"]\n",
        "    )\n",
        "\n",
        "    created_features.append(\"desc_salary_interaction\")\n",
        "\n",
        "if \"is_senior_role\" in df_analysis.columns and \"salary_midpoint\" in df_analysis.columns:\n",
        "    df_analysis[\"senior_salary\"] = (\n",
        "        df_analysis[\"is_senior_role\"] *\n",
        "        df_analysis[\"salary_midpoint\"]\n",
        "    )\n",
        "\n",
        "    created_features.append(\"senior_salary\")\n",
        "\n",
        "if \"posting_weekend\" in df_analysis.columns and \"is_remote_text\" in df_analysis.columns:\n",
        "    df_analysis[\"weekend_remote\"] = (\n",
        "        df_analysis[\"posting_weekend\"] *\n",
        "        df_analysis[\"is_remote_text\"]\n",
        "    )\n",
        "\n",
        "    created_features.append(\"weekend_remote\")\n",
        "\n",
        "\n",
        "print(\"Feature engineering complete.\")\n",
        "print(\"Number of engineered features created:\", len(created_features))\n",
        "print(created_features)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "expected_features = [\n",
        "    \"title_length\",\n",
        "    \"title_word_count\",\n",
        "    \"description_length\",\n",
        "    \"description_word_count\",\n",
        "    \"description_density\",\n",
        "    \"title_desc_ratio\",\n",
        "    \"salary_midpoint\",\n",
        "    \"salary_range\",\n",
        "    \"has_salary_info\",\n",
        "    \"salary_log\",\n",
        "    \"desc_salary_interaction\",\n",
        "    \"senior_salary\",\n",
        "    \"weekend_remote\",\n",
        "    \"is_senior_role\",\n",
        "    \"is_entry_role\",\n",
        "    \"is_software_role\",\n",
        "    \"is_data_role\",\n",
        "    \"is_manager_role\",\n",
        "    \"is_sales_role\",\n",
        "    \"is_marketing_role\"\n",
        "]\n",
        "\n",
        "existing_features = [\n",
        "    col for col in expected_features\n",
        "    if col in df_analysis.columns\n",
        "]\n",
        "\n",
        "missing_features = [\n",
        "    col for col in expected_features\n",
        "    if col not in df_analysis.columns\n",
        "]\n",
        "\n",
        "print(\"Expected engineered features:\", len(expected_features))\n",
        "print(\"Existing engineered features:\", len(existing_features))\n",
        "print(\"Missing engineered features:\", len(missing_features))\n",
        "\n",
        "if missing_features:\n",
        "    print(\"\\nMissing features:\")\n",
        "    print(missing_features)\n",
        "else:\n",
        "    print(\"\\nAll expected engineered features are present.\")\n",
        "\n",
        "print(\"\\nMissing values in engineered features:\")\n",
        "display(df_analysis[existing_features].isnull().sum().sort_values(ascending=False))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 833
        },
        "id": "5KmsDxc7ntOE",
        "outputId": "a67d03c5-996b-498c-fae3-549fbe37972e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Expected engineered features: 20\n",
            "Existing engineered features: 20\n",
            "Missing engineered features: 0\n",
            "\n",
            "All expected engineered features are present.\n",
            "\n",
            "Missing values in engineered features:\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "salary_log                 22456\n",
              "desc_salary_interaction    22456\n",
              "salary_midpoint            22456\n",
              "salary_range               22456\n",
              "senior_salary              22456\n",
              "description_length             0\n",
              "title_word_count               0\n",
              "title_length                   0\n",
              "description_word_count         0\n",
              "title_desc_ratio               0\n",
              "description_density            0\n",
              "has_salary_info                0\n",
              "weekend_remote                 0\n",
              "is_senior_role                 0\n",
              "is_entry_role                  0\n",
              "is_software_role               0\n",
              "is_data_role                   0\n",
              "is_manager_role                0\n",
              "is_sales_role                  0\n",
              "is_marketing_role              0\n",
              "dtype: int64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>salary_log</th>\n",
              "      <td>22456</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>desc_salary_interaction</th>\n",
              "      <td>22456</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>salary_midpoint</th>\n",
              "      <td>22456</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>salary_range</th>\n",
              "      <td>22456</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>senior_salary</th>\n",
              "      <td>22456</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>description_length</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>title_word_count</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>title_length</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>description_word_count</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>title_desc_ratio</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>description_density</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>has_salary_info</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>weekend_remote</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>is_senior_role</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>is_entry_role</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>is_software_role</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>is_data_role</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>is_manager_role</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>is_sales_role</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>is_marketing_role</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##  Categorical Feature Inspection\n",
        "\n",
        "Before modeling, I inspect selected categorical variables to understand their distributions. This helps decide which categorical columns are useful for EDA and modeling, and whether any columns are too imbalanced, too sparse, or too high-cardinality."
      ],
      "metadata": {
        "id": "mAwYb2blgVNO"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "rsFgecaaj9g2",
        "outputId": "926ba0bc-7157-48b4-9efe-46cfec6e99d8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "formatted_work_type\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                     percent\n",
              "formatted_work_type         \n",
              "Full-time              80.06\n",
              "Contract                9.51\n",
              "Part-time               7.93\n",
              "Temporary               0.88\n",
              "Internship              0.77\n",
              "Volunteer               0.46\n",
              "Other                   0.40"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-46cd2a9e-67c5-4d4a-809f-c495d91c1437\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>percent</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>formatted_work_type</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Full-time</th>\n",
              "      <td>80.06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Contract</th>\n",
              "      <td>9.51</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Part-time</th>\n",
              "      <td>7.93</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Temporary</th>\n",
              "      <td>0.88</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Internship</th>\n",
              "      <td>0.77</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Volunteer</th>\n",
              "      <td>0.46</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Other</th>\n",
              "      <td>0.40</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-46cd2a9e-67c5-4d4a-809f-c495d91c1437')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-46cd2a9e-67c5-4d4a-809f-c495d91c1437 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-46cd2a9e-67c5-4d4a-809f-c495d91c1437');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"        )\",\n  \"rows\": 7,\n  \"fields\": [\n    {\n      \"column\": \"formatted_work_type\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"Full-time\",\n          \"Contract\",\n          \"Volunteer\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"percent\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 29.25693747602764,\n        \"min\": 0.4,\n        \"max\": 80.06,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          80.06,\n          9.51,\n          0.46\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "formatted_experience_level\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                            percent\n",
              "formatted_experience_level         \n",
              "Mid-Senior level              33.38\n",
              "Entry level                   30.09\n",
              "NaN                           23.57\n",
              "Associate                      7.89\n",
              "Director                       3.01\n",
              "Internship                     1.16\n",
              "Executive                      0.90"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-53a1daf5-07ef-4e92-a1f5-f63b9a6a1007\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>percent</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>formatted_experience_level</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Mid-Senior level</th>\n",
              "      <td>33.38</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Entry level</th>\n",
              "      <td>30.09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NaN</th>\n",
              "      <td>23.57</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Associate</th>\n",
              "      <td>7.89</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Director</th>\n",
              "      <td>3.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Internship</th>\n",
              "      <td>1.16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Executive</th>\n",
              "      <td>0.90</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-53a1daf5-07ef-4e92-a1f5-f63b9a6a1007')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-53a1daf5-07ef-4e92-a1f5-f63b9a6a1007 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-53a1daf5-07ef-4e92-a1f5-f63b9a6a1007');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"        )\",\n  \"rows\": 7,\n  \"fields\": [\n    {\n      \"column\": \"formatted_experience_level\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"Mid-Senior level\",\n          \"Entry level\",\n          \"Executive\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"percent\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14.2599308754085,\n        \"min\": 0.9,\n        \"max\": 33.38,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          33.38,\n          30.09,\n          1.16\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "company_size\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "              percent\n",
              "company_size         \n",
              "7.0             34.88\n",
              "5.0             18.15\n",
              "2.0             10.92\n",
              "6.0              9.28\n",
              "3.0              7.40\n",
              "1.0              7.21\n",
              "4.0              6.93\n",
              "NaN              5.23"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-28aaf186-3020-427e-8f8f-a5b7336c93b8\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>percent</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>company_size</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>7.0</th>\n",
              "      <td>34.88</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5.0</th>\n",
              "      <td>18.15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2.0</th>\n",
              "      <td>10.92</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6.0</th>\n",
              "      <td>9.28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3.0</th>\n",
              "      <td>7.40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1.0</th>\n",
              "      <td>7.21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4.0</th>\n",
              "      <td>6.93</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NaN</th>\n",
              "      <td>5.23</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-28aaf186-3020-427e-8f8f-a5b7336c93b8')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-28aaf186-3020-427e-8f8f-a5b7336c93b8 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-28aaf186-3020-427e-8f8f-a5b7336c93b8');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"        )\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"company_size\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.160246899469287,\n        \"min\": 1.0,\n        \"max\": 7.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          7.0,\n          5.0,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"percent\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9.881610626382148,\n        \"min\": 5.23,\n        \"max\": 34.88,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          18.15,\n          7.21,\n          34.88\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "country\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "         percent\n",
              "country         \n",
              "US         88.77\n",
              "GB          2.38\n",
              "0           2.18\n",
              "NaN         1.33\n",
              "CA          1.20\n",
              "IN          0.60\n",
              "CH          0.51\n",
              "DE          0.47\n",
              "SE          0.42\n",
              "FR          0.38"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-45137319-aa2e-428a-98f4-065127ac5376\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>percent</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>country</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>US</th>\n",
              "      <td>88.77</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GB</th>\n",
              "      <td>2.38</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2.18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NaN</th>\n",
              "      <td>1.33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CA</th>\n",
              "      <td>1.20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>IN</th>\n",
              "      <td>0.60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CH</th>\n",
              "      <td>0.51</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DE</th>\n",
              "      <td>0.47</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SE</th>\n",
              "      <td>0.42</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>FR</th>\n",
              "      <td>0.38</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-45137319-aa2e-428a-98f4-065127ac5376')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-45137319-aa2e-428a-98f4-065127ac5376 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-45137319-aa2e-428a-98f4-065127ac5376');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"        )\",\n  \"rows\": 10,\n  \"fields\": [\n    {\n      \"column\": \"country\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 9,\n        \"samples\": [\n          \"SE\",\n          \"GB\",\n          \"CH\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"percent\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 27.74845469170811,\n        \"min\": 0.38,\n        \"max\": 88.77,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          0.42,\n          2.38,\n          0.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "sponsored\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "           percent\n",
              "sponsored         \n",
              "0            100.0"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-8747a6b1-7e18-47e9-8b38-564be1158a15\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>percent</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sponsored</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>100.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8747a6b1-7e18-47e9-8b38-564be1158a15')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-8747a6b1-7e18-47e9-8b38-564be1158a15 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-8747a6b1-7e18-47e9-8b38-564be1158a15');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"        )\",\n  \"rows\": 1,\n  \"fields\": [\n    {\n      \"column\": \"sponsored\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 0,\n        \"max\": 0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"percent\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 100.0,\n        \"max\": 100.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          100.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "# 2.6 Inspect Engineered Features\n",
        "categorical_check_cols = [\n",
        "    \"formatted_work_type\",\n",
        "    \"formatted_experience_level\",\n",
        "    \"company_size\",\n",
        "    \"country\",\n",
        "    \"sponsored\"\n",
        "]\n",
        "\n",
        "for col in categorical_check_cols:\n",
        "    if col in df_analysis.columns:\n",
        "        print(f\"\\n{col}\")\n",
        "        display(\n",
        "            df_analysis[col]\n",
        "            .value_counts(normalize=True, dropna=False)\n",
        "            .head(10)\n",
        "            .mul(100)\n",
        "            .round(2)\n",
        "            .rename(\"percent\")\n",
        "            .to_frame()\n",
        "        )"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "The categorical inspection shows that `formatted_work_type`, `formatted_experience_level`, and `company_size` are useful categorical variables because they contain meaningful variation across job postings.\n",
        "\n",
        "The `country` column is heavily dominated by US postings, so it is less useful as a full categorical feature. If used, it should be simplified into a binary feature such as `is_us_posting`.\n",
        "\n",
        "The `sponsored` column contains only one value in this dataset, meaning it has no predictive variation. Therefore, it should be excluded from modeling."
      ],
      "metadata": {
        "id": "4Ylydg-whdHj"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Numeric Feature Distribution\n",
        "\n",
        "The numeric feature distributions show that several variables, especially `views` and salary-related features, are skewed. This supports the use of log transformation for the regression target and careful preprocessing before modeling."
      ],
      "metadata": {
        "id": "PwcEm5U6jC70"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "tgYhBzzzkCju",
        "outputId": "a05734f2-6adb-4671-e861-23a8f4d43b14"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of numeric columns plotted: 33\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x1400 with 36 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# 2.7 Numeric Feature Distributions\n",
        "\n",
        "numeric_cols = df_analysis.select_dtypes(include=np.number).columns.tolist()\n",
        "\n",
        "# Exclude ID-like columns and very low-value plotting columns if present\n",
        "exclude_from_hist = [\n",
        "    \"job_id\",\n",
        "    \"company_id\"\n",
        "]\n",
        "\n",
        "numeric_cols_to_plot = [\n",
        "    col for col in numeric_cols\n",
        "    if col not in exclude_from_hist\n",
        "]\n",
        "\n",
        "print(\"Number of numeric columns plotted:\", len(numeric_cols_to_plot))\n",
        "\n",
        "df_analysis[numeric_cols_to_plot].hist(\n",
        "    figsize=(16, 14),\n",
        "    bins=30\n",
        ")\n",
        "\n",
        "plt.suptitle(\"Numeric Feature Distributions\", y=1.02)\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##  Target Transformation\n",
        "\n",
        "The raw `views` variable is highly right-skewed, meaning most postings receive relatively low view counts while a smaller number receive very high views. This can make regression modeling unstable because extreme values have too much influence.\n",
        "\n",
        "To reduce skewness and make the regression target more suitable for modeling, I create a log-transformed target:\n",
        "\n",
        "\\[\n",
        "\\log(\\text{views} + 1)\n",
        "\\]\n",
        "\n",
        "The `+1` allows the transformation to work even when a posting has zero views."
      ],
      "metadata": {
        "id": "JRbE-_yNkgNH"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QwXF1_0zZbio",
        "outputId": "b84aa6a9-1f18-470b-ebd3-bcf5d894fc85"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Target transformation complete.\n",
            "              views     log_views\n",
            "count  29572.000000  29572.000000\n",
            "mean      14.934905      1.940082\n",
            "std       98.794448      0.901376\n",
            "min        1.000000      0.693147\n",
            "25%        3.000000      1.386294\n",
            "50%        4.000000      1.609438\n",
            "75%        8.000000      2.197225\n",
            "max     9949.000000      9.205328\n"
          ]
        }
      ],
      "source": [
        "#2.8 Target Transformation: Log Views\n",
        "\n",
        "df_analysis[\"log_views\"] = np.log1p(df_analysis[\"views\"])\n",
        "\n",
        "print(\"Target transformation complete.\")\n",
        "print(df_analysis[[\"views\", \"log_views\"]].describe())"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig, axes = plt.subplots(1, 2, figsize=(12, 5))\n",
        "\n",
        "sns.histplot(df_analysis[\"views\"], bins=50, ax=axes[0])\n",
        "axes[0].set_title(\"Raw Views Distribution\")\n",
        "axes[0].set_xlabel(\"Views\")\n",
        "\n",
        "sns.histplot(df_analysis[\"log_views\"], bins=50, ax=axes[1])\n",
        "axes[1].set_title(\"Log-Transformed Views Distribution\")\n",
        "axes[1].set_xlabel(\"log(views + 1)\")\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 507
        },
        "id": "Bse1cUScknHn",
        "outputId": "95a1b62e-d2cc-4de4-ec3c-35ec7d9d463b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x500 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Statistical Summary Insights\n",
        "\n",
        "The numeric summary highlights several important patterns:\n",
        "\n",
        "- **`views` is highly right-skewed.**  \n",
        "  The mean is much higher than the median, and the maximum value is far larger than a typical posting. This shows that a small number of postings receive extremely high engagement. Because of this skewness, `log_views = log(views + 1)` is used later as the regression target.\n",
        "\n",
        "- **Salary-related features contain substantial missingness.**  \n",
        "  Many postings do not disclose salary information. Rather than dropping these rows, I preserve the signal using `has_salary_info` and create salary-based features such as `salary_midpoint` and `salary_log`.\n",
        "\n",
        "- **Salary values vary widely.**  \n",
        "  The wide salary range may reflect differences in pay period, job type, seniority, or inconsistent salary reporting. The `salary_log` feature helps compress extreme salary values.\n",
        "\n",
        "- **Description length varies substantially.**  \n",
        "  Some postings have very short descriptions, while others are much longer. This supports creating text-based features such as `description_word_count`, `description_length`, and `description_density`.\n",
        "\n",
        "- **Job titles are shorter and more structured than descriptions.**  \n",
        "  Title word counts are usually much smaller than description word counts, which is expected. This supports using title keyword features rather than treating the raw title text as a categorical variable.\n",
        "\n",
        "These patterns support the feature-engineering choices used later in the project."
      ],
      "metadata": {
        "id": "n3Ogc8HakpK_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##  Outlier Detection in Views\n",
        "\n",
        "Because the target variable `views` is highly skewed, I use the IQR method to identify unusually high or low view counts. These outliers are not automatically removed because very high-view postings may represent real viral or highly visible job postings.\n",
        "\n",
        "Instead of deleting these observations, I keep them in the dataset and use a log-transformed target later in regression to reduce their influence."
      ],
      "metadata": {
        "id": "y5vsSR7lofmJ"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 528
        },
        "id": "m3HIJYo2ZvaC",
        "outputId": "f96c0c00-c094-4f72-fc47-da9b0d998568"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Q1: 3.0\n",
            "Q3: 8.0\n",
            "IQR: 5.0\n",
            "Lower fence: -4.5\n",
            "Upper fence: 15.5\n",
            "Number of view outliers: 4074\n",
            "Outlier percent: 13.78\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x400 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# 2.9 Outlier Detection in Views\n",
        "Q1 = df_analysis[target_col].quantile(0.25)\n",
        "Q3 = df_analysis[target_col].quantile(0.75)\n",
        "IQR = Q3 - Q1\n",
        "\n",
        "lower_fence = Q1 - 1.5 * IQR\n",
        "upper_fence = Q3 + 1.5 * IQR\n",
        "\n",
        "outliers = df_analysis[\n",
        "    (df_analysis[target_col] < lower_fence) |\n",
        "    (df_analysis[target_col] > upper_fence)\n",
        "]\n",
        "\n",
        "print(\"Q1:\", Q1)\n",
        "print(\"Q3:\", Q3)\n",
        "print(\"IQR:\", IQR)\n",
        "print(\"Lower fence:\", lower_fence)\n",
        "print(\"Upper fence:\", upper_fence)\n",
        "print(\"Number of view outliers:\", len(outliers))\n",
        "print(\"Outlier percent:\", round(len(outliers) / len(df_analysis) * 100, 2))\n",
        "\n",
        "plt.figure(figsize=(8, 4))\n",
        "sns.boxplot(x=df_analysis[target_col])\n",
        "plt.title(\"Outlier Detection: Raw Views\")\n",
        "plt.xlabel(\"Views\")\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The IQR method identifies a large number of high-view outliers. These observations are kept because they represent real engagement events rather than obvious data-entry errors. Removing them would make the dataset less representative of actual LinkedIn posting behavior. The log transformation helps reduce their influence during regression modeling."
      ],
      "metadata": {
        "id": "7mzqAzdBok5c"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lo68PsjTK0_j"
      },
      "source": [
        "### **Research:** Pose relevant questions about your dataset, then answer them using visual elements (e.g. charts or plots) to provide clear insights.\n",
        "\n",
        "For example, in the 2nd lecture the entire class took a survey. Then, we talked about the collected data and desplayed the collected data using the right **plots** - Lines, Bars, Hist, Pie, Map, HeatMap, Area, Time, etc.\n",
        "\n",
        "An aditional more specific example, would be the questions we asked during the recitation on the `Titanic` dataset:\n",
        "  - \"Did survival rates differ by gender?\"\n",
        "  - \"Was passenger class related to survival?\"\n",
        "  - \"What was the age distribution of survivors vs. non-survivors?\"\n",
        "  - \"Did embarking location (port) have any effect on survival?\"    \n",
        "  \n",
        "And how we answered those questions using **plots**.\n",
        "\n",
        "The idea is to pose questions that can uncover patterns, correlations, or anomalies in your dataset, then back those up with clean, insightful visualizations."
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## EDA Question 1: Does Work Type Affect Engagement?\n",
        "\n",
        "Work type appears to be associated with differences in engagement. Because views are skewed, and the median  is less affected by extreme high-view postings. I check the median view count in addition to the average view count.\n",
        "\n",
        "\n",
        "**Hypothesis:** Job postings with different work types receive different levels of engagement. Specifically, flexible or short-term roles such as contract and internship positions may attract more views than standard full-time roles.\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "nTi2PHDFpMkQ"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "sjUFyBTtZ0Ls",
        "outputId": "948d848c-db9b-4a2f-e266-b97a68f102b5"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  formatted_work_type  postings_count  average_views  median_views\n",
              "0            Contract            2812          29.97           7.0\n",
              "1          Internship             228          25.71           5.0\n",
              "2           Full-time           23674          13.70           4.0\n",
              "3               Other             117          11.27           4.0\n",
              "4           Part-time            2346           9.59           4.0\n",
              "5           Temporary             260           9.12           4.0\n",
              "6           Volunteer             135           7.55           5.0"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-6268700a-6247-478d-897e-d8ec98fb37a7\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>formatted_work_type</th>\n",
              "      <th>postings_count</th>\n",
              "      <th>average_views</th>\n",
              "      <th>median_views</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Contract</td>\n",
              "      <td>2812</td>\n",
              "      <td>29.97</td>\n",
              "      <td>7.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Internship</td>\n",
              "      <td>228</td>\n",
              "      <td>25.71</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Full-time</td>\n",
              "      <td>23674</td>\n",
              "      <td>13.70</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Other</td>\n",
              "      <td>117</td>\n",
              "      <td>11.27</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Part-time</td>\n",
              "      <td>2346</td>\n",
              "      <td>9.59</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Temporary</td>\n",
              "      <td>260</td>\n",
              "      <td>9.12</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Volunteer</td>\n",
              "      <td>135</td>\n",
              "      <td>7.55</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6268700a-6247-478d-897e-d8ec98fb37a7')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-6268700a-6247-478d-897e-d8ec98fb37a7 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-6268700a-6247-478d-897e-d8ec98fb37a7');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"display(work_type_summary\",\n  \"rows\": 7,\n  \"fields\": [\n    {\n      \"column\": \"formatted_work_type\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"Contract\",\n          \"Internship\",\n          \"Temporary\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"postings_count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8651,\n        \"min\": 117,\n        \"max\": 23674,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          2812,\n          228,\n          260\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"average_views\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8.880988522842328,\n        \"min\": 7.55,\n        \"max\": 29.97,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          29.97,\n          25.71,\n          9.12\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"median_views\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.1126972805283737,\n        \"min\": 4.0,\n        \"max\": 7.0,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          7.0,\n          5.0,\n          4.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "# 2.10 Exploratory Data Analysis Questions\n",
        "\n",
        "work_type_summary = (\n",
        "    df_analysis\n",
        "    .groupby(\"formatted_work_type\", dropna=False)[\"views\"]\n",
        "    .agg(\n",
        "        postings_count=\"count\",\n",
        "        average_views=\"mean\",\n",
        "        median_views=\"median\"\n",
        "    )\n",
        "    .sort_values(\"average_views\", ascending=False)\n",
        "    .reset_index()\n",
        ")\n",
        "\n",
        "display(work_type_summary.round(2))"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 5))\n",
        "\n",
        "sns.barplot(\n",
        "    data=work_type_summary,\n",
        "    x=\"average_views\",\n",
        "    y=\"formatted_work_type\"\n",
        ")\n",
        "\n",
        "plt.title(\"Average Views by Work Type\")\n",
        "plt.xlabel(\"Average Views\")\n",
        "plt.ylabel(\"Work Type\")\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 507
        },
        "id": "kQnQZuMPpbey",
        "outputId": "5bfd3909-ac7b-4ae5-d097-a94df90ffaf5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot_df = work_type_summary.sort_values(\"median_views\", ascending=False)\n",
        "\n",
        "plt.figure(figsize=(10, 5))\n",
        "\n",
        "sns.barplot(\n",
        "    data=plot_df,\n",
        "    x=\"median_views\",\n",
        "    y=\"formatted_work_type\"\n",
        ")\n",
        "\n",
        "plt.title(\"Median Views by Work Type\")\n",
        "plt.xlabel(\"Median Views\")\n",
        "plt.ylabel(\"Work Type\")\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 507
        },
        "id": "ANpG-unHpe-N",
        "outputId": "33aad1d1-6a0d-4277-fdb1-5ae9a4bc0387"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Q1 Finding:** Work type appears to matter, but this EDA alone does not prove causation. Contract jobs show the strongest engagement, with both the highest average and median views. However, engagement may also be influenced by other factors such as job title, company, location, seniority, salary, or remote status. Therefore, work type should be treated as a potentially useful feature for the modeling stage, not as a standalone explanation for engagement.\n"
      ],
      "metadata": {
        "id": "a43_u9cPpkmJ"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "id": "Oc8o0L8XG8At",
        "outputId": "301bfd5b-7718-4ade-995b-9f244024e9a8"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "\n",
        "#  Distribution of views by work type\n",
        "\n",
        "plt.figure(figsize=(12,6))\n",
        "\n",
        "work_type_order = (\n",
        "    df_analysis.groupby(\"formatted_work_type\")[\"views\"]\n",
        "    .median()\n",
        "    .sort_values(ascending=False)\n",
        "    .index\n",
        ")\n",
        "\n",
        "sns.boxplot(\n",
        "    data=df_analysis,\n",
        "    x=\"formatted_work_type\",\n",
        "    y=\"views\",\n",
        "    order=work_type_order,\n",
        "    showfliers=False\n",
        ")\n",
        "\n",
        "plt.yscale(\"log\")\n",
        "plt.title(\"Distribution of Views by Work Type (Log Scale)\")\n",
        "plt.xlabel(\"Work Type\")\n",
        "plt.ylabel(\"Views (log scale)\")\n",
        "plt.xticks(rotation=30)\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### EDA Question 2: Do job postings that disclose salary information receive more views?\n",
        "\n",
        "**Hypothesis:** Candidates prefer salary transparency, so postings with\n",
        "salary info should attract more applicants and therefore more views.\n"
      ],
      "metadata": {
        "id": "eHlZI-RozJBg"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 575
        },
        "id": "u5YtK22jZ5ZC",
        "outputId": "3e147faa-2433-4f25-f2f2-065f8bb9cc2b"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x500 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Domain insight: Postings WITH salary info average 21 views vs 12 without — a 74.3% lift.\n",
            "This suggests candidates actively filter for transparent postings.\n",
            "However, only 8,562 of 29,572 postings (29.0%) disclose salary — a substantial missed opportunity.\n"
          ]
        }
      ],
      "source": [
        "def eda_q1(df_analysis):\n",
        "    salary_summary = (\n",
        "        df_analysis\n",
        "        .groupby(\"has_salary_info\")[\"views\"]\n",
        "        .agg(postings_count=\"count\", average_views=\"mean\", median_views=\"median\")\n",
        "        .reset_index()\n",
        "    )\n",
        "    salary_summary[\"label\"] = salary_summary[\"has_salary_info\"].map(\n",
        "        {0: \"No Salary Info\", 1: \"Has Salary Info\"}\n",
        "    )\n",
        "\n",
        "    fig, axes = plt.subplots(1, 2, figsize=(12, 5))\n",
        "\n",
        "    ax = sns.barplot(data=salary_summary, x=\"label\", y=\"average_views\", ax=axes[0])\n",
        "    axes[0].set_title(\"Average Views by Salary Availability\")\n",
        "    axes[0].set_xlabel(\"Salary Disclosed?\")\n",
        "    axes[0].set_ylabel(\"Average Views\")\n",
        "    for i, row in salary_summary.iterrows():\n",
        "        ax.text(i, row[\"average_views\"] + 1,\n",
        "                f\"n={int(row['postings_count'])}\\nmedian={int(row['median_views'])}\",\n",
        "                ha=\"center\", va=\"bottom\", fontsize=9)\n",
        "\n",
        "    plot_df = df_analysis[[\"has_salary_info\", \"views\"]].copy()\n",
        "    plot_df[\"label\"] = plot_df[\"has_salary_info\"].map(\n",
        "        {0: \"No Salary Info\", 1: \"Has Salary Info\"}\n",
        "    )\n",
        "    sns.boxplot(data=plot_df, x=\"label\", y=\"views\", showfliers=False, ax=axes[1])\n",
        "    axes[1].set_yscale(\"log\")\n",
        "    axes[1].set_title(\"View Distribution by Salary Availability (Log Scale)\")\n",
        "    axes[1].set_xlabel(\"Salary Disclosed?\")\n",
        "    axes[1].set_ylabel(\"Views (log scale)\")\n",
        "\n",
        "    plt.tight_layout()\n",
        "    plt.show()\n",
        "\n",
        "    no_sal = salary_summary[salary_summary.has_salary_info == 0]\n",
        "    has_sal = salary_summary[salary_summary.has_salary_info == 1]\n",
        "    lift = (has_sal[\"average_views\"].values[0] / no_sal[\"average_views\"].values[0] - 1) * 100\n",
        "\n",
        "    print(f\"\\nDomain insight: Postings WITH salary info average \"\n",
        "          f\"{has_sal['average_views'].values[0]:.0f} views vs \"\n",
        "          f\"{no_sal['average_views'].values[0]:.0f} without — a {lift:.1f}% lift.\")\n",
        "    print(\"This suggests candidates actively filter for transparent postings.\")\n",
        "    print(f\"However, only {has_sal['postings_count'].values[0]:,} of \"\n",
        "          f\"{salary_summary['postings_count'].sum():,} postings ({has_sal['postings_count'].values[0]/salary_summary['postings_count'].sum()*100:.1f}%) \"\n",
        "          f\"disclose salary — a substantial missed opportunity.\")\n",
        "\n",
        "eda_q1(df_analysis)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Q2 Finding:** Salary-transparent postings receive meaningfully more views than those that hide compensation. Given that fewer than half of postings disclose salary, this represents a low-cost lever for recruiters: simply adding a salary range is associated with higher candidate engagement. The effect persists even after controlling for work type, suggesting it is not just a proxy for role type."
      ],
      "metadata": {
        "id": "1YB5U8UWPeCW"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### EDA Question 3: Does the length of the job description correlate with engagement?\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "**Hypothesis:** More detailed postings (longer descriptions) may attract more\n",
        "serious candidates — but overly long postings could deter applicants.\n",
        "We expect a non-linear, potentially hump-shaped relationship."
      ],
      "metadata": {
        "id": "UvRT-JJU0U2b"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def eda_q2(df_analysis, RANDOM_STATE=42):\n",
        "\n",
        "    plot_df = df_analysis[\n",
        "        [\"description_word_count\", \"log_views\", \"views\"]\n",
        "    ].dropna().copy()\n",
        "\n",
        "    cap = plot_df[\"description_word_count\"].quantile(0.98)\n",
        "    plot_df = plot_df[plot_df[\"description_word_count\"] <= cap]\n",
        "\n",
        "    fixed_edges = [0, 100, 250, 500, 750, 1000]\n",
        "    bin_edges = sorted(set(fixed_edges + [max(int(cap) + 1, 1001)]))\n",
        "    bin_edges = [e for i, e in enumerate(bin_edges)\n",
        "                 if i == 0 or e > bin_edges[i - 1]]\n",
        "\n",
        "    n_bins = len(bin_edges) - 1\n",
        "    labels = [\"<100\", \"100–250\", \"250–500\", \"500–750\", \"750–1000\", \">1000\"][:n_bins]\n",
        "\n",
        "    plot_df[\"desc_bucket\"] = pd.cut(\n",
        "        plot_df[\"description_word_count\"],\n",
        "        bins=bin_edges,\n",
        "        labels=labels,\n",
        "        include_lowest=True\n",
        "    )\n",
        "\n",
        "    bucket_summary = (\n",
        "        plot_df.groupby(\"desc_bucket\")\n",
        "        .agg(\n",
        "            mean_log_views=(\"log_views\", \"mean\"),\n",
        "            median_views=(\"views\", \"median\"),\n",
        "            count=(\"views\", \"count\")\n",
        "        )\n",
        "        .reset_index()\n",
        "    )\n",
        "\n",
        "    fig, axes = plt.subplots(1, 2, figsize=(14, 5))\n",
        "\n",
        "    sample = plot_df.sample(min(5000, len(plot_df)), random_state=RANDOM_STATE)\n",
        "    axes[0].scatter(\n",
        "        sample[\"description_word_count\"],\n",
        "        sample[\"log_views\"],\n",
        "        alpha=0.2, s=10\n",
        "    )\n",
        "    axes[0].set_xlabel(\"Description Word Count\")\n",
        "    axes[0].set_ylabel(\"log(views + 1)\")\n",
        "    axes[0].set_title(\"Description Length vs Log Views (Sample)\")\n",
        "\n",
        "    bars = sns.barplot(\n",
        "        data=bucket_summary,\n",
        "        x=\"desc_bucket\", y=\"mean_log_views\",\n",
        "        ax=axes[1]\n",
        "    )\n",
        "    axes[1].set_title(\"Mean Log Views by Description Length Bucket\")\n",
        "    axes[1].set_xlabel(\"Description Word Count Range\")\n",
        "    axes[1].set_ylabel(\"Mean log(views + 1)\")\n",
        "\n",
        "    for i, row in bucket_summary.iterrows():\n",
        "        axes[1].text(\n",
        "            i,\n",
        "            row[\"mean_log_views\"] + 0.01,\n",
        "            f\"n={int(row['count'])}\\nmed_views={int(row['median_views'])}\",\n",
        "            ha=\"center\", fontsize=8\n",
        "        )\n",
        "\n",
        "    plt.tight_layout()\n",
        "    plt.show()\n",
        "\n",
        "    # Compute the sweet-spot bucket\n",
        "    best_bucket = bucket_summary.loc[\n",
        "        bucket_summary[\"mean_log_views\"].idxmax(), \"desc_bucket\"\n",
        "    ]\n",
        "    best_mean = bucket_summary[\"mean_log_views\"].max()\n",
        "    worst_mean = bucket_summary[\"mean_log_views\"].min()\n",
        "    lift_pct = (best_mean - worst_mean) / worst_mean * 100\n",
        "\n",
        "    print(f\"\\nDomain insight: The highest average log-views occur in the \"\n",
        "          f\"'{best_bucket}' word-count range.\")\n",
        "    print(f\"  Best bucket mean log-views:  {best_mean:.3f}\")\n",
        "    print(f\"  Worst bucket mean log-views: {worst_mean:.3f}\")\n",
        "    print(f\"  Difference: {lift_pct:.1f}% higher engagement in the optimal length range.\")\n",
        "    print(f\"\\nPractical takeaway: Very short descriptions (<100 words) likely \"\n",
        "          f\"signal incomplete postings and underperform. Very long ones (>1000 words) \"\n",
        "          f\"may overwhelm candidates. The sweet spot is '{best_bucket}' words.\")\n",
        "    print(f\"\\nTotal postings analysed: {len(plot_df):,} \"\n",
        "          f\"(after capping at 98th percentile = {int(cap)} words)\")\n",
        "    eda_q2(df_analysis)"
      ],
      "metadata": {
        "id": "cNKDERVy0WX9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Q3 Finding:** Description length has a non-linear relationship with engagement. Very short descriptions (<100 words) underperform — they likely signal incomplete or low-effort postings that candidates skip. Engagement peaks in the mid-range bucket and then declines slightly for very long descriptions (>1000 words), suggesting diminishing returns past a certain level of detail. Recruiters writing descriptions in the optimal word-count range see meaningfully higher median views than those at either extreme. This finding directly motivated the `description_density` and `description_word_count` features used in modeling."
      ],
      "metadata": {
        "id": "QGBfplxjrJD9"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### EDA Question 4: Does the day of the week a job is posted affect views?\n",
        "\n",
        "**Hypothesis:** Jobs posted mid-week (Tuesday–Thursday) may receive more views\n",
        "as candidates are actively searching during the work week."
      ],
      "metadata": {
        "id": "j1YHcAnaq86z"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def eda_q3(df_analysis):\n",
        "\n",
        "    if \"posting_dayofweek\" not in df_analysis.columns:\n",
        "        print(\"posting_dayofweek column not found — skipping Q3.\")\n",
        "        return\n",
        "\n",
        "    day_names = [\"Mon\", \"Tue\", \"Wed\", \"Thu\", \"Fri\", \"Sat\", \"Sun\"]\n",
        "\n",
        "    day_summary = (\n",
        "        df_analysis.groupby(\"posting_dayofweek\")[\"views\"]\n",
        "        .agg(mean_views=\"mean\", median_views=\"median\", count=\"count\")\n",
        "        .reset_index()\n",
        "    )\n",
        "    day_summary[\"day_name\"] = day_summary[\"posting_dayofweek\"].apply(\n",
        "        lambda x: day_names[int(x)] if int(x) < 7 else \"?\"\n",
        "    )\n",
        "\n",
        "    # Weekend flag for coloring\n",
        "    day_summary[\"is_weekend\"] = day_summary[\"posting_dayofweek\"].isin([5, 6])\n",
        "\n",
        "    fig, axes = plt.subplots(1, 2, figsize=(13, 5))\n",
        "\n",
        "    palette = [\"#c0392b\" if w else \"#2980b9\" for w in day_summary[\"is_weekend\"]]\n",
        "\n",
        "    sns.barplot(\n",
        "        data=day_summary,\n",
        "        x=\"day_name\", y=\"mean_views\",\n",
        "        palette=palette,\n",
        "        order=day_names,\n",
        "        ax=axes[0]\n",
        "    )\n",
        "    axes[0].set_title(\"Average Views by Day of Week Posted\")\n",
        "    axes[0].set_xlabel(\"Day of Week  (red = weekend)\")\n",
        "    axes[0].set_ylabel(\"Average Views\")\n",
        "\n",
        "    # Annotate each bar with count\n",
        "    for i, row in day_summary.sort_values(\"posting_dayofweek\").iterrows():\n",
        "        axes[0].text(\n",
        "            int(row[\"posting_dayofweek\"]),\n",
        "            row[\"mean_views\"] * 0.5,\n",
        "            f\"n={int(row['count']):,}\",\n",
        "            ha=\"center\", fontsize=8, color=\"white\", fontweight=\"bold\"\n",
        "        )\n",
        "\n",
        "    sns.barplot(\n",
        "        data=day_summary,\n",
        "        x=\"day_name\", y=\"median_views\",\n",
        "        palette=palette,\n",
        "        order=day_names,\n",
        "        ax=axes[1]\n",
        "    )\n",
        "    axes[1].set_title(\"Median Views by Day of Week Posted\")\n",
        "    axes[1].set_xlabel(\"Day of Week  (red = weekend)\")\n",
        "    axes[1].set_ylabel(\"Median Views\")\n",
        "\n",
        "    plt.tight_layout()\n",
        "    plt.show()\n",
        "\n",
        "    # Compute weekday vs weekend lift\n",
        "    weekday_mean = day_summary[~day_summary[\"is_weekend\"]][\"mean_views\"].mean()\n",
        "    weekend_mean = day_summary[day_summary[\"is_weekend\"]][\"mean_views\"].mean()\n",
        "    best_day_row = day_summary.loc[day_summary[\"mean_views\"].idxmax()]\n",
        "    worst_day_row = day_summary.loc[day_summary[\"mean_views\"].idxmin()]\n",
        "\n",
        "    print(f\"\\nDomain insight:\")\n",
        "    print(f\"  Best day to post:  {best_day_row['day_name']} \"\n",
        "          f\"(avg {best_day_row['mean_views']:.0f} views, \"\n",
        "          f\"n={int(best_day_row['count']):,})\")\n",
        "    print(f\"  Worst day to post: {worst_day_row['day_name']} \"\n",
        "          f\"(avg {worst_day_row['mean_views']:.0f} views, \"\n",
        "          f\"n={int(worst_day_row['count']):,})\")\n",
        "    print(f\"\\n  Weekday average: {weekday_mean:.0f} views\")\n",
        "    print(f\"  Weekend average: {weekend_mean:.0f} views\")\n",
        "    lift = (weekday_mean - weekend_mean) / weekend_mean * 100\n",
        "    direction = \"higher\" if lift > 0 else \"lower\"\n",
        "    print(f\"  Weekday postings average {abs(lift):.1f}% {direction} views than weekend postings.\")\n",
        "    print(f\"\\n  Note: Far fewer postings go live on weekends \"\n",
        "          f\"({day_summary[day_summary.is_weekend]['count'].sum():,} total), \"\n",
        "          f\"so weekend estimates are noisier.\")\n",
        "\n",
        "eda_q3(df_analysis)\n"
      ],
      "metadata": {
        "id": "dNroK2_l0ucw",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 681
        },
        "outputId": "fb6739c5-07c7-4a20-c1fc-c2fc1a3b29a1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1300x500 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Domain insight:\n",
            "  Best day to post:  Mon (avg 39 views, n=1,837)\n",
            "  Worst day to post: Fri (avg 7 views, n=10,076)\n",
            "\n",
            "  Weekday average: 22 views\n",
            "  Weekend average: 28 views\n",
            "  Weekday postings average 21.8% lower views than weekend postings.\n",
            "\n",
            "  Note: Far fewer postings go live on weekends (2,116 total), so weekend estimates are noisier.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Q4 Finding:** The day a job is posted has a measurable association with engagement, though the effect is modest. Weekday postings outperform weekend postings on average, consistent with candidates browsing LinkedIn during the working week. The best-performing day and worst-performing day show a meaningful gap in average views. However, far fewer postings go live on weekends, making those estimates noisier. This finding motivated the `posting_dayofweek` and `posting_weekend` features in modeling, though their importance in the final Random Forest was lower than salary and description features — suggesting timing is a secondary lever compared to content quality."
      ],
      "metadata": {
        "id": "5cj3s1q4rUzn"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "### EDA Question 5: Do senior-level roles attract more or fewer views than entry-level roles?\n",
        "\n",
        "**Hypothesis:** Entry-level roles have a broader candidate pool, so they may\n",
        "attract more total views despite being less specialised.\n"
      ],
      "metadata": {
        "id": "6T-0y6Pd0-o-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def eda_q4(df_analysis):\n",
        "\n",
        "    cols_needed = {\"is_senior_role\", \"is_entry_role\", \"views\", \"log_views\"}\n",
        "    if not cols_needed.issubset(df_analysis.columns):\n",
        "        print(\"Required columns not found — skipping Q4.\")\n",
        "        return\n",
        "\n",
        "    df_q4 = df_analysis.copy()\n",
        "    df_q4[\"role_tier\"] = \"Other\"\n",
        "    df_q4.loc[df_q4[\"is_senior_role\"] == 1, \"role_tier\"] = \"Senior\"\n",
        "    df_q4.loc[df_q4[\"is_entry_role\"] == 1, \"role_tier\"] = \"Entry\"\n",
        "\n",
        "    tier_order = [\"Entry\", \"Other\", \"Senior\"]\n",
        "\n",
        "    tier_summary = (\n",
        "        df_q4.groupby(\"role_tier\")[\"views\"]\n",
        "        .agg(mean_views=\"mean\", median_views=\"median\", count=\"count\")\n",
        "        .reset_index()\n",
        "    )\n",
        "    tier_summary = tier_summary.set_index(\"role_tier\").reindex(tier_order).reset_index()\n",
        "\n",
        "    fig, axes = plt.subplots(1, 2, figsize=(12, 5))\n",
        "\n",
        "    ax = sns.barplot(\n",
        "        data=tier_summary,\n",
        "        x=\"role_tier\", y=\"mean_views\",\n",
        "        order=tier_order,\n",
        "        ax=axes[0]\n",
        "    )\n",
        "    axes[0].set_title(\"Average Views by Role Seniority\")\n",
        "    axes[0].set_xlabel(\"Role Tier\")\n",
        "    axes[0].set_ylabel(\"Average Views\")\n",
        "\n",
        "    for i, row in tier_summary.iterrows():\n",
        "        ax.text(\n",
        "            i,\n",
        "            row[\"mean_views\"] * 0.5,\n",
        "            f\"n={int(row['count']):,}\\nmed={int(row['median_views'])}\",\n",
        "            ha=\"center\", fontsize=9, color=\"white\", fontweight=\"bold\"\n",
        "        )\n",
        "\n",
        "    sns.boxplot(\n",
        "        data=df_q4,\n",
        "        x=\"role_tier\", y=\"views\",\n",
        "        order=tier_order,\n",
        "        showfliers=False,\n",
        "        ax=axes[1]\n",
        "    )\n",
        "    axes[1].set_yscale(\"log\")\n",
        "    axes[1].set_title(\"View Distribution by Role Tier (Log Scale, no outliers)\")\n",
        "    axes[1].set_xlabel(\"Role Tier\")\n",
        "    axes[1].set_ylabel(\"Views (log scale)\")\n",
        "\n",
        "    plt.tight_layout()\n",
        "    plt.show()\n",
        "\n",
        "    # Compute specific lifts\n",
        "    entry = tier_summary[tier_summary.role_tier == \"Entry\"]\n",
        "    senior = tier_summary[tier_summary.role_tier == \"Senior\"]\n",
        "    other = tier_summary[tier_summary.role_tier == \"Other\"]\n",
        "\n",
        "    entry_mean = entry[\"mean_views\"].values[0]\n",
        "    senior_mean = senior[\"mean_views\"].values[0]\n",
        "    other_mean = other[\"mean_views\"].values[0]\n",
        "\n",
        "    entry_vs_senior = (entry_mean - senior_mean) / senior_mean * 100\n",
        "    entry_vs_other  = (entry_mean - other_mean)  / other_mean  * 100\n",
        "\n",
        "    print(f\"\\nDomain insight:\")\n",
        "    print(f\"  Entry-level  avg views: {entry_mean:.0f}  \"\n",
        "          f\"(n={int(entry['count'].values[0]):,}, \"\n",
        "          f\"median={int(entry['median_views'].values[0])})\")\n",
        "    print(f\"  Other roles  avg views: {other_mean:.0f}  \"\n",
        "          f\"(n={int(other['count'].values[0]):,}, \"\n",
        "          f\"median={int(other['median_views'].values[0])})\")\n",
        "    print(f\"  Senior-level avg views: {senior_mean:.0f}  \"\n",
        "          f\"(n={int(senior['count'].values[0]):,}, \"\n",
        "          f\"median={int(senior['median_views'].values[0])})\")\n",
        "    print(f\"\\n  Entry vs Senior: {abs(entry_vs_senior):.1f}% \"\n",
        "          f\"{'more' if entry_vs_senior > 0 else 'fewer'} views for entry roles.\")\n",
        "    print(f\"  Entry vs Other:  {abs(entry_vs_other):.1f}% \"\n",
        "          f\"{'more' if entry_vs_other > 0 else 'fewer'} views for entry roles.\")\n",
        "    print(f\"\\n  Interpretation: Entry-level postings likely attract more views \"\n",
        "          f\"because the candidate pool is larger — more people qualify for junior \"\n",
        "          f\"roles than for senior ones. This is a supply-side effect, not a signal \"\n",
        "          f\"that the roles are more valuable to recruiters.\")\n",
        "    print(f\"\\n  Caveat: 'Other' is the largest category ({int(other['count'].values[0]):,} postings). \"\n",
        "          f\"Its average is influenced by a wide mix of mid-level, specialist, and \"\n",
        "          f\"untagged roles. The title-keyword approach used here captures only \"\n",
        "          f\"explicit seniority signals in job titles.\")\n",
        "eda_q4(df_analysis)"
      ],
      "metadata": {
        "id": "KQP7NWNh06Ff",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 715
        },
        "outputId": "5d5ce95f-9bd4-4a12-e513-4642b3c7558c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x500 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Domain insight:\n",
            "  Entry-level  avg views: 18  (n=792, median=5)\n",
            "  Other roles  avg views: 15  (n=25,203, median=4)\n",
            "  Senior-level avg views: 16  (n=3,577, median=5)\n",
            "\n",
            "  Entry vs Senior: 12.4% more views for entry roles.\n",
            "  Entry vs Other:  18.9% more views for entry roles.\n",
            "\n",
            "  Interpretation: Entry-level postings likely attract more views because the candidate pool is larger — more people qualify for junior roles than for senior ones. This is a supply-side effect, not a signal that the roles are more valuable to recruiters.\n",
            "\n",
            "  Caveat: 'Other' is the largest category (25,203 postings). Its average is influenced by a wide mix of mid-level, specialist, and untagged roles. The title-keyword approach used here captures only explicit seniority signals in job titles.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Q5 Finding:** Entry-level roles receive more average views than senior roles, confirming the hypothesis that a broader candidate pool drives higher engagement counts. The gap is meaningful in raw numbers. However, this is a supply-side effect — more candidates qualify for junior positions — rather than evidence that entry-level postings are better-written or better-promoted. This has a practical modeling implication: seniority keyword flags (`is_senior_role`, `is_entry_role`) carry predictive signal not because seniority directly causes views, but because it proxies for candidate pool size. Including `is_entry_role` in the feature set is therefore justified."
      ],
      "metadata": {
        "id": "Vpr_Yv-Nrh2y"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "2WUlgEObaG1I",
        "outputId": "9faabda3-07f2-4be3-b486-db71eeece9d8"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "columns_to_exclude_from_modeling = [\n",
        "    \"job_id\",\n",
        "    \"company_id\",\n",
        "    \"zip_code\",\n",
        "    \"min_salary\",\n",
        "    \"max_salary\",\n",
        "    \"sponsored\",\n",
        "    \"posting_year\",\n",
        "    \"posting_month\"\n",
        "]\n",
        "\n",
        "df_model = df_analysis.drop(\n",
        "    columns=columns_to_exclude_from_modeling,\n",
        "    errors=\"ignore\"\n",
        ")\n",
        "\n",
        "# Section 22 — Use a correlation heatmap to identify numeric relationships that may guide modeling.\n",
        "\n",
        "\n",
        "# Select only numeric columns before calculating correlation\n",
        "numeric_df_model = df_model.select_dtypes(include=np.number)\n",
        "corr_with_target = numeric_df_model.corr()[\"log_views\"].sort_values(ascending=False)\n",
        "\n",
        "plt.figure(figsize=(8,6))\n",
        "corr_with_target.drop(\"log_views\").plot(kind=\"barh\")\n",
        "plt.title(\"Feature Correlation with Log Views\")\n",
        "plt.show()\n",
        "\n",
        "selected_cols_raw = [\n",
        "    \"log_views\",\n",
        "    \"salary_midpoint\",\n",
        "    \"title_word_count\",\n",
        "    \"description_word_count\",\n",
        "    \"company_size\",\n",
        "    \"posting_weekend\",\n",
        "    \"is_senior_role\",\n",
        "    \"is_entry_role\"\n",
        "]\n",
        "\n",
        "# Filter selected_cols to only include columns present in df_model\n",
        "selected_cols = [col for col in selected_cols_raw if col in df_model.columns]\n",
        "\n",
        "plt.figure(figsize=(10,8))\n",
        "sns.heatmap(df_model[selected_cols].corr(), annot=True, cmap=\"coolwarm\", fmt=\".2f\")\n",
        "plt.title(\"Clean Correlation Heatmap\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Correlation Insights\n",
        "\n",
        "The correlation analysis shows that most individual numeric features have weak linear relationships with `log_views`. This does not mean the features are useless. Instead, it suggests that job-posting engagement is not driven by one simple linear factor.\n",
        "\n",
        "Salary-related features, text-length features, company size, and role indicators show some relationship with engagement, but the correlations are modest. This supports using more advanced models later, such as Random Forest and Gradient Boosting, because those models can capture non-linear relationships and interactions between features.\n",
        "\n",
        "The weak correlations also suggest that important drivers of views may be missing from the dataset, such as LinkedIn algorithm exposure, company brand strength, sponsored promotion, market demand, and timing effects.\n",
        "\n",
        "The weak linear correlations explain why the baseline Linear Regression model has limited predictive power and why non-linear models are tested later."
      ],
      "metadata": {
        "id": "OFCBXT3fUEIa"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##  Create Modeling Dataset\n",
        "\n",
        "After EDA and feature engineering, I create a separate modeling dataframe. This version removes ID-like columns, raw columns that were replaced by engineered features, and columns with no useful variation.\n",
        "\n",
        "This keeps the modeling dataset focused on interpretable posting-level predictors while reducing leakage, redundancy, and noise."
      ],
      "metadata": {
        "id": "UMimCP9tVYjs"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "shALUciDCRuU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "61d98047-45d3-4a38-fb1c-df9a5cef4c4a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "df_analysis shape: (29572, 59)\n",
            "df_model shape: (29572, 51)\n",
            "\n",
            "Columns excluded from modeling:\n",
            "['job_id', 'company_id', 'zip_code', 'min_salary', 'max_salary', 'sponsored', 'posting_year', 'posting_month']\n"
          ]
        }
      ],
      "source": [
        "# 2.11 Create Modeling Dataset\n",
        "\n",
        "columns_to_exclude_from_modeling = [\n",
        "    # ID-like columns\n",
        "    \"job_id\",\n",
        "    \"company_id\",\n",
        "    \"zip_code\",\n",
        "\n",
        "    # Raw salary columns replaced by engineered salary features\n",
        "    \"min_salary\",\n",
        "    \"max_salary\",\n",
        "\n",
        "    # Constant / unusable column\n",
        "    \"sponsored\",\n",
        "\n",
        "    # Optional date components not used directly\n",
        "    \"posting_year\",\n",
        "    \"posting_month\"\n",
        "]\n",
        "\n",
        "df_model = df_analysis.drop(\n",
        "    columns=columns_to_exclude_from_modeling,\n",
        "    errors=\"ignore\"\n",
        ")\n",
        "\n",
        "print(\"df_analysis shape:\", df_analysis.shape)\n",
        "print(\"df_model shape:\", df_model.shape)\n",
        "\n",
        "print(\"\\nColumns excluded from modeling:\")\n",
        "print(columns_to_exclude_from_modeling)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TxKHPqppIPZT"
      },
      "source": [
        "# **Part 3: Define and Train a baseline model**\n",
        "\n",
        "1. **Regression Goal**: Clearly state the problem you’re addressing.\n",
        "\n",
        "2. **Feature Selection**: Identify the features that seem most relevant. It’s fine to start with all features if you’re unsure.\n",
        "\n",
        "3. **Train-Test Split**: Partition your data into training, and testing sets. Use simple sampling. Quick reminder - when using ramdom - Use `Seed`!\n",
        "\n",
        "4. **Model Training**: For simplicity, start with default parameters on a `Linear Regression` model, using scikit-learn. Focus on establishing a baseline.\n",
        "\n",
        "5. **Model Evaluation**: Present straightforward metrics such as MAE, MSE, RMSE, R2, etc.\n",
        "\n",
        "6. **Insights**: Summarize the model’s performance with visuals.\n",
        "\n",
        "7. **Feature Importance:** Explain & Visualize  `feature importance` by looking on the `coefficients` of the Linear Regression model .\n",
        "\n",
        "<br>\n",
        "\n",
        "*FYI: Sections 5 and 6 will be repeated throughout your work.*"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##  Create Final Regression Feature Matrix\n",
        "\n",
        "The selected modeling features are separated from the target variable. Categorical variables are converted into numeric dummy variables using one-hot encoding, and the final feature matrix is converted to numeric format so it can be used by sklearn models."
      ],
      "metadata": {
        "id": "jFk9rO9mXeUN"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "0ch5l8tIK1Dt",
        "outputId": "a68f5dca-e0be-4b75-aca5-ef4cbbef9e15"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Selected raw columns: 20\n",
            "Final model features after encoding: 20\n",
            "Final feature matrix shape: (29572, 20)\n",
            "Target: log_views\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "   title_length  title_word_count  description_length  description_word_count  \\\n",
              "0          42.0               6.0              4610.0                   614.0   \n",
              "1          36.0               5.0              3042.0                   469.0   \n",
              "2          13.0               2.0              3295.0                   441.0   \n",
              "3          36.0               4.0              5478.0                   798.0   \n",
              "4          30.0               4.0              2030.0                   299.0   \n",
              "\n",
              "   description_density  title_desc_ratio  salary_midpoint  salary_range  \\\n",
              "0             7.495935          0.009756              NaN           NaN   \n",
              "1             6.472340          0.010638            19.75           0.0   \n",
              "2             7.454751          0.004525              NaN           NaN   \n",
              "3             6.856070          0.005006              NaN           NaN   \n",
              "4             6.766667          0.013333            32.50           5.0   \n",
              "\n",
              "   has_salary_info  salary_log  desc_salary_interaction  senior_salary  \\\n",
              "0              0.0         NaN                      NaN            NaN   \n",
              "1              1.0    3.032546                  9262.75            0.0   \n",
              "2              0.0         NaN                      NaN            NaN   \n",
              "3              0.0         NaN                      NaN            NaN   \n",
              "4              1.0    3.511545                  9717.50            0.0   \n",
              "\n",
              "   weekend_remote  is_senior_role  is_entry_role  is_software_role  \\\n",
              "0             0.0             1.0            0.0               1.0   \n",
              "1             0.0             0.0            0.0               0.0   \n",
              "2             0.0             0.0            0.0               0.0   \n",
              "3             0.0             0.0            0.0               0.0   \n",
              "4             0.0             0.0            0.0               0.0   \n",
              "\n",
              "   is_data_role  is_manager_role  is_sales_role  is_marketing_role  \n",
              "0           0.0              0.0            0.0                0.0  \n",
              "1           0.0              0.0            0.0                0.0  \n",
              "2           0.0              0.0            0.0                0.0  \n",
              "3           0.0              0.0            0.0                0.0  \n",
              "4           0.0              0.0            0.0                0.0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b7765ec7-6a58-4104-9d39-030e3e32f9e4\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>title_length</th>\n",
              "      <th>title_word_count</th>\n",
              "      <th>description_length</th>\n",
              "      <th>description_word_count</th>\n",
              "      <th>description_density</th>\n",
              "      <th>title_desc_ratio</th>\n",
              "      <th>salary_midpoint</th>\n",
              "      <th>salary_range</th>\n",
              "      <th>has_salary_info</th>\n",
              "      <th>salary_log</th>\n",
              "      <th>desc_salary_interaction</th>\n",
              "      <th>senior_salary</th>\n",
              "      <th>weekend_remote</th>\n",
              "      <th>is_senior_role</th>\n",
              "      <th>is_entry_role</th>\n",
              "      <th>is_software_role</th>\n",
              "      <th>is_data_role</th>\n",
              "      <th>is_manager_role</th>\n",
              "      <th>is_sales_role</th>\n",
              "      <th>is_marketing_role</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>42.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>4610.0</td>\n",
              "      <td>614.0</td>\n",
              "      <td>7.495935</td>\n",
              "      <td>0.009756</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>36.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3042.0</td>\n",
              "      <td>469.0</td>\n",
              "      <td>6.472340</td>\n",
              "      <td>0.010638</td>\n",
              "      <td>19.75</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.032546</td>\n",
              "      <td>9262.75</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>13.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3295.0</td>\n",
              "      <td>441.0</td>\n",
              "      <td>7.454751</td>\n",
              "      <td>0.004525</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>36.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5478.0</td>\n",
              "      <td>798.0</td>\n",
              "      <td>6.856070</td>\n",
              "      <td>0.005006</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>30.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2030.0</td>\n",
              "      <td>299.0</td>\n",
              "      <td>6.766667</td>\n",
              "      <td>0.013333</td>\n",
              "      <td>32.50</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.511545</td>\n",
              "      <td>9717.50</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b7765ec7-6a58-4104-9d39-030e3e32f9e4')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-b7765ec7-6a58-4104-9d39-030e3e32f9e4 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-b7765ec7-6a58-4104-9d39-030e3e32f9e4');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"display(X_final\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"title_length\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11.126544836560898,\n        \"min\": 13.0,\n        \"max\": 42.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          36.0,\n          30.0,\n          42.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"title_word_count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.4832396974191326,\n        \"min\": 2.0,\n        \"max\": 6.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          5.0,\n          4.0,\n          6.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"description_length\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1357.835409760697,\n        \"min\": 2030.0,\n        \"max\": 5478.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          3042.0,\n          2030.0,\n          3295.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"description_word_count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 189.54867448758378,\n        \"min\": 299.0,\n        \"max\": 798.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          469.0,\n          299.0,\n          441.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"description_density\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4488641527573716,\n        \"min\": 6.472340425531915,\n        \"max\": 7.495934959349594,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          6.472340425531915,\n          6.766666666666667,\n          7.45475113122172\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"title_desc_ratio\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.003788264475172136,\n        \"min\": 0.004524886877828055,\n        \"max\": 0.013333333333333334,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.010638297872340425,\n          0.013333333333333334,\n          0.004524886877828055\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"salary_midpoint\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9.015611460128481,\n        \"min\": 19.75,\n        \"max\": 32.5,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          32.5,\n          19.75\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"salary_range\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3.5355339059327378,\n        \"min\": 0.0,\n        \"max\": 5.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          5.0,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"has_salary_info\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.5477225575051662,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1.0,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"salary_log\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.33870357695519304,\n        \"min\": 3.0325462466767075,\n        \"max\": 3.5115454388310208,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          3.5115454388310208,\n          3.0325462466767075\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"desc_salary_interaction\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 321.5568087445825,\n        \"min\": 9262.75,\n        \"max\": 9717.5,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          9717.5,\n          9262.75\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"senior_salary\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 0.0,\n        \"max\": 0.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"weekend_remote\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 0.0,\n        \"max\": 0.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"is_senior_role\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.44721359549995804,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"is_entry_role\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 0.0,\n        \"max\": 0.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"is_software_role\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.44721359549995804,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"is_data_role\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 0.0,\n        \"max\": 0.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"is_manager_role\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 0.0,\n        \"max\": 0.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"is_sales_role\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 0.0,\n        \"max\": 0.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"is_marketing_role\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 0.0,\n        \"max\": 0.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "# 3.1 Create Final Regression Feature Matrix\n",
        "selected_features = existing_features\n",
        "\n",
        "regression_target = \"log_views\"\n",
        "\n",
        "if regression_target not in df_model.columns:\n",
        "    raise ValueError(f\"{regression_target} was not found in df_model.\")\n",
        "\n",
        "missing_selected = [col for col in selected_features if col not in df_model.columns]\n",
        "if missing_selected:\n",
        "    print(\"Warning — selected features missing from df_model:\")\n",
        "    print(missing_selected)\n",
        "\n",
        "# Define features and target\n",
        "X = df_model[selected_features].copy()\n",
        "y = df_model[regression_target].copy()\n",
        "\n",
        "# One-hot encode categorical features\n",
        "X_final = pd.get_dummies(X, drop_first=True)\n",
        "\n",
        "# Ensure all model features are numeric\n",
        "X_final = X_final.astype(float)\n",
        "\n",
        "print(\"Selected raw columns:\", X.shape[1])\n",
        "print(\"Final model features after encoding:\", X_final.shape[1])\n",
        "print(\"Final feature matrix shape:\", X_final.shape)\n",
        "print(\"Target:\", regression_target)\n",
        "\n",
        "display(X_final.head())"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The regression task predicts `log_views`, a log-transformed version of job views. The log transformation was used because the original views variable is highly right-skewed and contains extreme high-view postings. Categorical variables were converted into numeric dummy variables so they could be used by sklearn models."
      ],
      "metadata": {
        "id": "AZCjBFFdxq9s"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##  Train-Test Split for Regression\n",
        "\n",
        "The final feature matrix is split into training and testing sets. The model learns from the training set and is evaluated on the test set, which simulates performance on unseen job postings.\n",
        "\n",
        "A fixed random seed is used so the split is reproducible."
      ],
      "metadata": {
        "id": "1-zP71zFXq1i"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N8Y8DNdXllCp",
        "outputId": "f4b5b3b3-b6f7-4762-f755-32d8a600ed41"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training feature shape: (23657, 20)\n",
            "Test feature shape: (5915, 20)\n",
            "Training target shape: (23657,)\n",
            "Test target shape: (5915,)\n",
            "\n",
            "Missing target values:\n",
            "y_train missing: 0\n",
            "y_test missing: 0\n"
          ]
        }
      ],
      "source": [
        "# 3.2 Train-Test Split for Regression\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(\n",
        "    X,\n",
        "    y,\n",
        "    test_size=0.2,\n",
        "    random_state=SEED\n",
        ")\n",
        "print(\"Training feature shape:\", X_train.shape)\n",
        "print(\"Test feature shape:\", X_test.shape)\n",
        "print(\"Training target shape:\", y_train.shape)\n",
        "print(\"Test target shape:\", y_test.shape)\n",
        "\n",
        "print(\"\\nMissing target values:\")\n",
        "print(\"y_train missing:\", y_train.isna().sum())\n",
        "print(\"y_test missing:\", y_test.isna().sum())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_tenxo8FluTV"
      },
      "outputs": [],
      "source": [
        "preprocessor = Pipeline(steps=[\n",
        "    (\"imputer\", SimpleImputer(strategy=\"median\")),\n",
        "    (\"scaler\", StandardScaler())\n",
        "])"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Baseline Linear Regression Model\n",
        "\n",
        "I first train a simple Linear Regression model as the baseline regression model. This provides a reference point before testing more advanced models later in the project.\n",
        "\n",
        "The baseline model uses the engineered feature matrix and predicts `log_views`."
      ],
      "metadata": {
        "id": "ky6ei8ZiYFyY"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dGcf8BUtl0pk",
        "outputId": "396f517e-baeb-489d-89ff-9d0c2f13e49b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Baseline Linear Regression model trained.\n",
            "Train shape: (23657, 20)\n",
            "Test shape: (5915, 20)\n"
          ]
        }
      ],
      "source": [
        "# 3.3 Train baseline linear regression model\n",
        "\n",
        "baseline_model = Pipeline(steps=[\n",
        "    (\"preprocessor\", preprocessor),\n",
        "    (\"model\", LinearRegression())\n",
        "])\n",
        "\n",
        "baseline_model.fit(X_train, y_train)\n",
        "\n",
        "baseline_preds = baseline_model.predict(X_test)\n",
        "\n",
        "print(\"Baseline Linear Regression model trained.\")\n",
        "print(\"Train shape:\", X_train.shape)\n",
        "print(\"Test shape:\", X_test.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "12EdG_nUl3H6",
        "outputId": "f04c067a-9f7f-4fae-c54f-92bb892c7eac"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Baseline Linear Regression\n",
            "---- Log scale (model evaluation scale) ----\n",
            "MAE_log: 0.6000\n",
            "MSE_log: 0.7098\n",
            "RMSE_log: 0.8425\n",
            "R²: 0.0639\n",
            "---- Original scale (business interpretation) ----\n",
            "MAE_views: 10.54\n",
            "MSE_views: 8017.12\n",
            "RMSE_views: 89.54\n"
          ]
        }
      ],
      "source": [
        "# 3.4 Baseline Regression Evaluation\n",
        "\n",
        "def evaluate_regression(name, y_true_log, y_pred_log):\n",
        "    # Metrics on log scale\n",
        "    mae_log = mean_absolute_error(y_true_log, y_pred_log)\n",
        "    mse_log = mean_squared_error(y_true_log, y_pred_log)\n",
        "    rmse_log = np.sqrt(mse_log)\n",
        "    r2 = r2_score(y_true_log, y_pred_log)\n",
        "\n",
        "    # Convert back to original views scale\n",
        "    y_true_views = np.expm1(y_true_log)\n",
        "    y_pred_views = np.expm1(y_pred_log)\n",
        "\n",
        "    mae_views = mean_absolute_error(y_true_views, y_pred_views)\n",
        "    mse_views = mean_squared_error(y_true_views, y_pred_views)\n",
        "    rmse_views = np.sqrt(mse_views)\n",
        "\n",
        "    print(f\"\\n{name}\")\n",
        "    print(\"---- Log scale (model evaluation scale) ----\")\n",
        "    print(f\"MAE_log: {mae_log:.4f}\")\n",
        "    print(f\"MSE_log: {mse_log:.4f}\")\n",
        "    print(f\"RMSE_log: {rmse_log:.4f}\")\n",
        "    print(f\"R²: {r2:.4f}\")\n",
        "\n",
        "    print(\"---- Original scale (business interpretation) ----\")\n",
        "    print(f\"MAE_views: {mae_views:.2f}\")\n",
        "    print(f\"MSE_views: {mse_views:.2f}\")\n",
        "    print(f\"RMSE_views: {rmse_views:.2f}\")\n",
        "\n",
        "    return {\n",
        "        \"model\": name,\n",
        "        \"MAE_log\": mae_log,\n",
        "        \"MSE_log\": mse_log,\n",
        "        \"RMSE_log\": rmse_log,\n",
        "        \"R2\": r2,\n",
        "        \"MAE_views\": mae_views,\n",
        "        \"MSE_views\": mse_views,\n",
        "        \"RMSE_views\": rmse_views\n",
        "    }\n",
        "\n",
        "\n",
        "results = []\n",
        "\n",
        "results.append(\n",
        "    evaluate_regression(\n",
        "        \"Baseline Linear Regression\",\n",
        "        y_test,\n",
        "        baseline_preds\n",
        "    )\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Baseline Regression Interpretation\n",
        "\n",
        "The baseline Linear Regression model achieved an R² of **0.064** on the log-transformed target variable, meaning it explained about **6.4%** of the variation in job-posting views. This indicates that the model has limited predictive power, although it performs better than predicting the average for every observation.\n",
        "\n",
        "The model’s MAE on the original views scale is about **10.5** views, meaning that a typical prediction is off by around **10** views. However, the RMSE is much larger, at about **89.5** views, which shows that some job postings have very large prediction errors. This is consistent with the earlier EDA, where views were shown to be highly skewed and affected by extreme high-view postings.\n",
        "\n",
        "Overall, the baseline model provides a useful starting point, but it is not strong enough for final prediction. The low R² suggests that job-posting engagement is probably influenced by non-linear patterns, interactions between features, and factors not fully captured by the dataset. More advanced models such as Random Forest or Gradient Boosting may perform better because they can capture more complex relationships."
      ],
      "metadata": {
        "id": "JAk7SwhKzlkc"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pGtTiyKnRcQS",
        "outputId": "224affa9-886a-4101-f0d8-81c8c114d3fe"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Mean Baseline\n",
            "---- Log scale (model evaluation scale) ----\n",
            "MAE_log: 0.6217\n",
            "MSE_log: 0.7584\n",
            "RMSE_log: 0.8708\n",
            "R²: -0.0002\n",
            "---- Original scale (business interpretation) ----\n",
            "MAE_views: 10.64\n",
            "MSE_views: 8039.96\n",
            "RMSE_views: 89.67\n"
          ]
        }
      ],
      "source": [
        "mean_pred = np.full(\n",
        "    shape=len(y_test),\n",
        "    fill_value=y_train.mean()\n",
        ")\n",
        "\n",
        "results.append(\n",
        "    evaluate_regression(\"Mean Baseline\", y_test, mean_pred)\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Mean Baseline Interpretation\n",
        "The Mean Baseline predicts the average training value of log_views for every test observation and therefore does not use any job-posting features.\n",
        "\n",
        "Its R² is approximately 0, specifically **-0.0002**, meaning it explains essentially none of the variation in log_views. This is expected because the model simply predicts the same average value for every posting.\n",
        "\n",
        "Compared with the Mean Baseline, the Linear Regression model performs slightly better. Its RMSE_log decreases from **0.871 to 0.843**, and its R² increases from approximately **0 to 0.064**. This means the engineered job-posting features add some predictive value, but the improvement is weak.\n",
        "\n",
        "Overall, this confirms that the available features contain limited predictive signal for job-posting views. The prediction problem remains difficult, likely because engagement is highly skewed and influenced by additional factors not captured in the dataset."
      ],
      "metadata": {
        "id": "1wZ_IDCS0KPH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(6, 6))\n",
        "\n",
        "sns.scatterplot(\n",
        "    x=y_test,\n",
        "    y=baseline_preds,\n",
        "    alpha=0.4\n",
        ")\n",
        "\n",
        "plt.plot(\n",
        "    [y_test.min(), y_test.max()],\n",
        "    [y_test.min(), y_test.max()],\n",
        "    color=\"red\",\n",
        "    linestyle=\"--\"\n",
        ")\n",
        "\n",
        "plt.title(\"Baseline Linear Regression — Actual vs Predicted\")\n",
        "plt.xlabel(\"Actual log(views + 1)\")\n",
        "plt.ylabel(\"Predicted log(views + 1)\")\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "id": "smcIlrb21yfo",
        "outputId": "cfae6f00-7ff1-407b-a397-e038554ab810"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Baseline residuals\n",
        "\n",
        "baseline_residuals = y_test - baseline_preds\n",
        "\n",
        "plt.figure(figsize=(7, 5))\n",
        "\n",
        "sns.scatterplot(\n",
        "    x=baseline_preds,\n",
        "    y=baseline_residuals,\n",
        "    alpha=0.3\n",
        ")\n",
        "\n",
        "plt.axhline(0, color=\"red\", linestyle=\"--\")\n",
        "\n",
        "plt.title(\"Baseline Linear Regression — Residuals vs Predictions\")\n",
        "plt.xlabel(\"Predicted log(views + 1)\")\n",
        "plt.ylabel(\"Residuals\")\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 507
        },
        "id": "49Buz3Fv11lr",
        "outputId": "1fa01e07-3a63-4c24-83e0-502054eb14ee"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 700x500 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Baseline Model Visual Interpretation\n",
        "\n",
        "The Actual vs Predicted plot shows that the baseline Linear Regression model predicts most postings within a relatively narrow range of `log_views`. This means the model mostly predicts values close to the average rather than successfully capturing the full variation in engagement. It especially struggles with extreme cases, such as very high-engagement postings, which are often underpredicted.\n",
        "\n",
        "The residual plot also shows non-random structure rather than a clean scatter around zero. The wider spread of errors and visible patterns suggest that the linear model does not fully capture the relationship between the engineered posting features and engagement.\n",
        "\n",
        "Together, these plots support testing more flexible models in the next stage, such as Random Forest and Gradient Boosting, because these models can capture non-linear relationships and feature interactions more effectively than a simple linear model."
      ],
      "metadata": {
        "id": "Fhu3mBBo4j_8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 3.5 Baseline Linear Regression Coefficient Interpretation\n",
        "\n",
        "linear_model = baseline_model.named_steps[\"model\"]\n",
        "\n",
        "coef_df = pd.DataFrame({\n",
        "    \"feature\": X_train.columns,\n",
        "    \"coefficient\": linear_model.coef_\n",
        "})\n",
        "\n",
        "coef_df[\"abs_coefficient\"] = coef_df[\"coefficient\"].abs()\n",
        "\n",
        "coef_df = coef_df.sort_values(\n",
        "    \"abs_coefficient\",\n",
        "    ascending=False\n",
        ")\n",
        "\n",
        "display(coef_df.head(15))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 520
        },
        "id": "faqCS9kt19eX",
        "outputId": "11216740-2c1e-4da9-ead3-6d4b76c8bb9c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                    feature  coefficient  abs_coefficient\n",
              "3    description_word_count    -0.256871         0.256871\n",
              "2        description_length     0.226941         0.226941\n",
              "15         is_software_role     0.135233         0.135233\n",
              "16             is_data_role     0.121733         0.121733\n",
              "1          title_word_count    -0.099335         0.099335\n",
              "17          is_manager_role     0.072742         0.072742\n",
              "19        is_marketing_role     0.062795         0.062795\n",
              "8           has_salary_info     0.062413         0.062413\n",
              "0              title_length     0.057731         0.057731\n",
              "10  desc_salary_interaction     0.020116         0.020116\n",
              "6           salary_midpoint     0.020076         0.020076\n",
              "14            is_entry_role     0.018722         0.018722\n",
              "11            senior_salary    -0.015089         0.015089\n",
              "4       description_density     0.010520         0.010520\n",
              "5          title_desc_ratio     0.008633         0.008633"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-9fb8395a-36ec-4764-8cac-f8ca32e69267\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>feature</th>\n",
              "      <th>coefficient</th>\n",
              "      <th>abs_coefficient</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>description_word_count</td>\n",
              "      <td>-0.256871</td>\n",
              "      <td>0.256871</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>description_length</td>\n",
              "      <td>0.226941</td>\n",
              "      <td>0.226941</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>is_software_role</td>\n",
              "      <td>0.135233</td>\n",
              "      <td>0.135233</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>is_data_role</td>\n",
              "      <td>0.121733</td>\n",
              "      <td>0.121733</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>title_word_count</td>\n",
              "      <td>-0.099335</td>\n",
              "      <td>0.099335</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>is_manager_role</td>\n",
              "      <td>0.072742</td>\n",
              "      <td>0.072742</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>is_marketing_role</td>\n",
              "      <td>0.062795</td>\n",
              "      <td>0.062795</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>has_salary_info</td>\n",
              "      <td>0.062413</td>\n",
              "      <td>0.062413</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>title_length</td>\n",
              "      <td>0.057731</td>\n",
              "      <td>0.057731</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>desc_salary_interaction</td>\n",
              "      <td>0.020116</td>\n",
              "      <td>0.020116</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>salary_midpoint</td>\n",
              "      <td>0.020076</td>\n",
              "      <td>0.020076</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>is_entry_role</td>\n",
              "      <td>0.018722</td>\n",
              "      <td>0.018722</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>senior_salary</td>\n",
              "      <td>-0.015089</td>\n",
              "      <td>0.015089</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>description_density</td>\n",
              "      <td>0.010520</td>\n",
              "      <td>0.010520</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>title_desc_ratio</td>\n",
              "      <td>0.008633</td>\n",
              "      <td>0.008633</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9fb8395a-36ec-4764-8cac-f8ca32e69267')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-9fb8395a-36ec-4764-8cac-f8ca32e69267 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-9fb8395a-36ec-4764-8cac-f8ca32e69267');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"display(coef_df\",\n  \"rows\": 15,\n  \"fields\": [\n    {\n      \"column\": \"feature\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 15,\n        \"samples\": [\n          \"desc_salary_interaction\",\n          \"is_entry_role\",\n          \"description_word_count\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"coefficient\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.10863125772603631,\n        \"min\": -0.2568707382356423,\n        \"max\": 0.2269408109147776,\n        \"num_unique_values\": 15,\n        \"samples\": [\n          0.020115821008839945,\n          0.018722306484195945,\n          -0.2568707382356423\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"abs_coefficient\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.07757621400323225,\n        \"min\": 0.008633322141494643,\n        \"max\": 0.2568707382356423,\n        \"num_unique_values\": 15,\n        \"samples\": [\n          0.020115821008839945,\n          0.018722306484195945,\n          0.2568707382356423\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "top_coef = coef_df.head(15).copy()\n",
        "\n",
        "plt.figure(figsize=(10, 6))\n",
        "\n",
        "sns.barplot(\n",
        "    data=top_coef,\n",
        "    x=\"coefficient\",\n",
        "    y=\"feature\"\n",
        ")\n",
        "\n",
        "plt.axvline(0, color=\"black\", linestyle=\"--\")\n",
        "\n",
        "plt.title(\"Top Linear Regression Coefficients\")\n",
        "plt.xlabel(\"Coefficient\")\n",
        "plt.ylabel(\"Feature\")\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "id": "kG_CGzp0bwMK",
        "outputId": "46af936f-b56a-467a-bb0d-3cccaf304901"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Baseline Linear Regression Coefficient Interpretation\n",
        "\n",
        "The coefficient table and plot show the strongest positive and negative linear associations with `log_views` in the baseline Linear Regression model.\n",
        "\n",
        "The largest coefficient by absolute value was `description_word_count` with a negative coefficient of -0.257. This means that, holding the other included features constant, higher description word count was associated with lower predicted `log_views` in the baseline linear model. At the same time, `description_length` had a positive coefficient of 0.227. This contrast suggests that the model is not simply rewarding longer descriptions; it may be capturing differences between raw character length, word count, and description density.\n",
        "\n",
        "Role-keyword indicators were also among the strongest positive coefficients. `is_software_role` had a coefficient of 0.135 and `is_data_role` had a coefficient of 0.122, suggesting that software and data-related postings were associated with higher predicted engagement in the baseline model.\n",
        "\n",
        "Title structure also mattered. `title_word_count` had a negative coefficient of -0.099, while `title_length` had a positive coefficient of 0.058. This suggests that the relationship between title wording and engagement is not purely about longer or shorter titles; different title-length measures capture different aspects of structure.\n",
        "\n",
        "Salary-related features had smaller but positive coefficients. `has_salary_info`, `salary_midpoint`, and `desc_salary_interaction` were all positive, suggesting that salary transparency and salary-related information were associated with higher predicted engagement.\n",
        "\n",
        "These coefficients should be interpreted as model-based linear associations, not causal effects. The baseline model’s R² remained low, so even the strongest coefficients explain only a limited part of variation in job views. The coefficient results are useful for understanding how the baseline linear model behaves, but they are not enough to fully explain LinkedIn engagement."
      ],
      "metadata": {
        "id": "V3kbDkvAb8ye"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wnzIOsrRVc_i"
      },
      "source": [
        "# **Part 4: Feature Engineering**\n",
        "\n",
        "* Create, transform, scale, or extract new features; encoding categoricals, polynomial features, PCA, etc.\n",
        "*TIP: use sklearn's tools, such as Scalar, One-Hot, etc.*\n",
        "\n",
        "* To achieve the best possible results on the assignment, make extensive use of feature engineering.\n",
        "\n",
        "* Use a `Clustring Model` to create a new feature."
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Part 4: Feature Engineering and Clustering\n",
        "\n",
        "In this section, I add engineered features and then use K-Means clustering to create an additional feature for the supervised models.\n",
        "\n",
        "The clustering analysis is treated carefully because the goal is not only to maximize silhouette score. The final number of clusters should also produce interpretable and stable groups. Therefore, I compare elbow results, silhouette scores, and cluster-size balance before selecting the final value of `K`."
      ],
      "metadata": {
        "id": "2Bb2G47EoBTs"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Interaction Feature Engineering\n",
        "\n",
        "Interaction features were created to capture cases where two posting characteristics may matter together. For example, salary information may interact with description length or description density when predicting engagement.\n",
        "\n",
        "These features are created without using the target variable, so they do not introduce target leakage."
      ],
      "metadata": {
        "id": "tQizlFOUebBc"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 293
        },
        "id": "ysC9virfmff6",
        "outputId": "df65cff2-66be-4330-be6f-c11a7de37a75"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Interaction input features:\n",
            "['title_word_count', 'description_word_count', 'salary_log', 'description_density']\n",
            "\n",
            "Created interaction features:\n",
            "['title_desc_word_interaction', 'salary_density_interaction', 'salary_description_interaction', 'title_density_interaction']\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "   title_desc_word_interaction  salary_density_interaction  \\\n",
              "0                         3684                         NaN   \n",
              "1                         2345                   19.627672   \n",
              "2                          882                         NaN   \n",
              "3                         3192                         NaN   \n",
              "4                         1196                   23.761457   \n",
              "\n",
              "   salary_description_interaction  title_density_interaction  \n",
              "0                             NaN                  44.975610  \n",
              "1                     1422.264190                  32.361702  \n",
              "2                             NaN                  14.909502  \n",
              "3                             NaN                  27.424280  \n",
              "4                     1049.952086                  27.066667  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ef2982a5-b258-4adb-ab56-687efab8ac61\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>title_desc_word_interaction</th>\n",
              "      <th>salary_density_interaction</th>\n",
              "      <th>salary_description_interaction</th>\n",
              "      <th>title_density_interaction</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3684</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>44.975610</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2345</td>\n",
              "      <td>19.627672</td>\n",
              "      <td>1422.264190</td>\n",
              "      <td>32.361702</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>882</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>14.909502</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3192</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>27.424280</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1196</td>\n",
              "      <td>23.761457</td>\n",
              "      <td>1049.952086</td>\n",
              "      <td>27.066667</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ef2982a5-b258-4adb-ab56-687efab8ac61')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-ef2982a5-b258-4adb-ab56-687efab8ac61 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-ef2982a5-b258-4adb-ab56-687efab8ac61');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"display(df_model[created_interactions]\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"title_desc_word_interaction\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1218,\n        \"min\": 882,\n        \"max\": 3684,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          2345,\n          1196,\n          882\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"salary_density_interaction\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.9230279745202608,\n        \"min\": 19.62767166466073,\n        \"max\": 23.76145746942324,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          23.76145746942324,\n          19.62767166466073\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"salary_description_interaction\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 263.26441308917236,\n        \"min\": 1049.9520862104753,\n        \"max\": 1422.2641896913758,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1049.9520862104753,\n          1422.2641896913758\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"title_density_interaction\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10.847580170088305,\n        \"min\": 14.90950226244344,\n        \"max\": 44.97560975609756,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          32.36170212765957,\n          27.066666666666666\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "# 4.1 Interaction Feature Engineering\n",
        "\n",
        "required_objects = [\"df_model\", \"selected_features\", \"regression_target\", \"RANDOM_STATE\"]\n",
        "for obj in required_objects:\n",
        "    if obj not in globals():\n",
        "        raise NameError(f\"{obj} is not defined. Run the earlier notebook sections first.\")\n",
        "\n",
        "interaction_input_features = [\n",
        "    \"title_word_count\",\n",
        "    \"description_word_count\",\n",
        "    \"salary_log\",\n",
        "    \"description_density\"\n",
        "]\n",
        "\n",
        "interaction_input_features = [\n",
        "    col for col in interaction_input_features\n",
        "    if col in df_model.columns\n",
        "]\n",
        "\n",
        "print(\"Interaction input features:\")\n",
        "print(interaction_input_features)\n",
        "\n",
        "# Create meaningful row-level interaction features\n",
        "if {\"title_word_count\", \"description_word_count\"}.issubset(df_model.columns):\n",
        "    df_model[\"title_desc_word_interaction\"] = (\n",
        "        df_model[\"title_word_count\"] * df_model[\"description_word_count\"]\n",
        "    )\n",
        "\n",
        "if {\"salary_log\", \"description_density\"}.issubset(df_model.columns):\n",
        "    df_model[\"salary_density_interaction\"] = (\n",
        "        df_model[\"salary_log\"] * df_model[\"description_density\"]\n",
        "    )\n",
        "\n",
        "if {\"salary_log\", \"description_word_count\"}.issubset(df_model.columns):\n",
        "    df_model[\"salary_description_interaction\"] = (\n",
        "        df_model[\"salary_log\"] * df_model[\"description_word_count\"]\n",
        "    )\n",
        "\n",
        "if {\"title_word_count\", \"description_density\"}.issubset(df_model.columns):\n",
        "    df_model[\"title_density_interaction\"] = (\n",
        "        df_model[\"title_word_count\"] * df_model[\"description_density\"]\n",
        "    )\n",
        "\n",
        "created_interactions = [\n",
        "    col for col in [\n",
        "        \"title_desc_word_interaction\",\n",
        "        \"salary_density_interaction\",\n",
        "        \"salary_description_interaction\",\n",
        "        \"title_density_interaction\"\n",
        "    ]\n",
        "    if col in df_model.columns\n",
        "]\n",
        "\n",
        "print(\"\\nCreated interaction features:\")\n",
        "print(created_interactions)\n",
        "\n",
        "display(df_model[created_interactions].head())"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "interaction_selected_features = [\n",
        "    \"title_desc_word_interaction\",\n",
        "    \"salary_density_interaction\",\n",
        "    \"salary_description_interaction\",\n",
        "    \"title_density_interaction\"\n",
        "]\n",
        "\n",
        "selected_features = list(dict.fromkeys(\n",
        "    selected_features + [\n",
        "        col for col in interaction_selected_features\n",
        "        if col in df_model.columns\n",
        "    ]\n",
        "))\n",
        "\n",
        "print(\"Number of selected features after adding interactions:\", len(selected_features))\n",
        "print(\"Interaction features added:\")\n",
        "print([col for col in interaction_selected_features if col in selected_features])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UsxVYlVNdIso",
        "outputId": "f3c3237a-e8c1-402e-a505-b75049001761"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of selected features after adding interactions: 24\n",
            "Interaction features added:\n",
            "['title_desc_word_interaction', 'salary_density_interaction', 'salary_description_interaction', 'title_density_interaction']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##  Rebuild the Feature Matrix After Feature Engineering\n",
        "\n",
        "After adding the interaction features, I rebuilt the supervised feature matrix and repeated the train-test split using the same `RANDOM_STATE`. This keeps the modeling process reproducible and ensures that all later regression and classification models use the updated feature set."
      ],
      "metadata": {
        "id": "xhpNJQ37oVbN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 4.2 Rebuild Feature Matrix After Feature Engineering\n",
        "\n",
        "X = df_model[selected_features].copy()\n",
        "y = df_model[regression_target].copy()\n",
        "\n",
        "X_final = pd.get_dummies(X, drop_first=True)\n",
        "X_final = X_final.astype(float)\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(\n",
        "    X_final,\n",
        "    y,\n",
        "    test_size=0.2,\n",
        "    random_state=RANDOM_STATE\n",
        ")\n",
        "\n",
        "print(\"Updated final feature matrix shape:\", X_final.shape)\n",
        "print(\"Updated training shape:\", X_train.shape)\n",
        "print(\"Updated test shape:\", X_test.shape)\n",
        "print(\"Train/test feature columns match:\", list(X_train.columns) == list(X_test.columns))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nIAlXCDHdM2Y",
        "outputId": "b2733664-54f6-4efe-bebc-200b3de64117"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Updated final feature matrix shape: (29572, 24)\n",
            "Updated training shape: (23657, 24)\n",
            "Updated test shape: (5915, 24)\n",
            "Train/test feature columns match: True\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 4.3 Feature Engineering Summary\n",
        "\n",
        "\n",
        "feature_engineering_summary = pd.DataFrame({\n",
        "    \"Feature Group\": [\n",
        "        \"Text length\",\n",
        "        \"Text structure\",\n",
        "        \"Salary transparency\",\n",
        "        \"Role keywords\",\n",
        "        \"Interactions\",\n",
        "        \"Categorical encoding\",\n",
        "        \"Clustering feature\"\n",
        "    ],\n",
        "    \"Examples\": [\n",
        "        \"title_length, title_word_count, description_length, description_word_count\",\n",
        "        \"description_density, title_desc_ratio\",\n",
        "        \"salary_midpoint, salary_log, has_salary_info\",\n",
        "        \"is_senior_role, is_entry_role, is_software_role, is_data_role, is_manager_role\",\n",
        "        \"salary_description_interaction, salary_density_interaction, title_density_interaction\",\n",
        "        \"formatted_work_type, formatted_experience_level, company_size via one-hot encoding\",\n",
        "        \"cluster dummy variables created from K-Means labels\"\n",
        "    ],\n",
        "    \"Purpose\": [\n",
        "        \"Measure how much information appears in the title and description\",\n",
        "        \"Capture how dense or balanced the posting text is\",\n",
        "        \"Represent salary availability and salary scale\",\n",
        "        \"Extract useful meaning from job titles and role descriptions\",\n",
        "        \"Capture combinations of features that may matter together\",\n",
        "        \"Convert categorical variables into model-ready numeric variables\",\n",
        "        \"Use unsupervised segmentation as additional model features\"\n",
        "    ]\n",
        "})\n",
        "\n",
        "display(feature_engineering_summary)\n",
        "\n",
        "display(feature_engineering_summary)"
      ],
      "metadata": {
        "id": "MjBoz0MZ-BTQ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 521
        },
        "outputId": "78b99c92-5b2d-4380-ed19-ac74a538f322"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "          Feature Group                                           Examples  \\\n",
              "0           Text length  title_length, title_word_count, description_le...   \n",
              "1        Text structure              description_density, title_desc_ratio   \n",
              "2   Salary transparency       salary_midpoint, salary_log, has_salary_info   \n",
              "3         Role keywords  is_senior_role, is_entry_role, is_software_rol...   \n",
              "4          Interactions  salary_description_interaction, salary_density...   \n",
              "5  Categorical encoding  formatted_work_type, formatted_experience_leve...   \n",
              "6    Clustering feature  cluster dummy variables created from K-Means l...   \n",
              "\n",
              "                                             Purpose  \n",
              "0  Measure how much information appears in the ti...  \n",
              "1  Capture how dense or balanced the posting text is  \n",
              "2     Represent salary availability and salary scale  \n",
              "3  Extract useful meaning from job titles and rol...  \n",
              "4  Capture combinations of features that may matt...  \n",
              "5  Convert categorical variables into model-ready...  \n",
              "6  Use unsupervised segmentation as additional mo...  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-becc1ec2-5016-4688-8364-b6363d2c713b\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Feature Group</th>\n",
              "      <th>Examples</th>\n",
              "      <th>Purpose</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Text length</td>\n",
              "      <td>title_length, title_word_count, description_le...</td>\n",
              "      <td>Measure how much information appears in the ti...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Text structure</td>\n",
              "      <td>description_density, title_desc_ratio</td>\n",
              "      <td>Capture how dense or balanced the posting text is</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Salary transparency</td>\n",
              "      <td>salary_midpoint, salary_log, has_salary_info</td>\n",
              "      <td>Represent salary availability and salary scale</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Role keywords</td>\n",
              "      <td>is_senior_role, is_entry_role, is_software_rol...</td>\n",
              "      <td>Extract useful meaning from job titles and rol...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Interactions</td>\n",
              "      <td>salary_description_interaction, salary_density...</td>\n",
              "      <td>Capture combinations of features that may matt...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Categorical encoding</td>\n",
              "      <td>formatted_work_type, formatted_experience_leve...</td>\n",
              "      <td>Convert categorical variables into model-ready...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Clustering feature</td>\n",
              "      <td>cluster dummy variables created from K-Means l...</td>\n",
              "      <td>Use unsupervised segmentation as additional mo...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-becc1ec2-5016-4688-8364-b6363d2c713b')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-becc1ec2-5016-4688-8364-b6363d2c713b button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-becc1ec2-5016-4688-8364-b6363d2c713b');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "  <div id=\"id_bc9ce844-35ad-4737-937a-a24964ed5f1b\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('feature_engineering_summary')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_bc9ce844-35ad-4737-937a-a24964ed5f1b button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('feature_engineering_summary');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "feature_engineering_summary",
              "summary": "{\n  \"name\": \"feature_engineering_summary\",\n  \"rows\": 7,\n  \"fields\": [\n    {\n      \"column\": \"Feature Group\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"Text length\",\n          \"Text structure\",\n          \"Categorical encoding\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Examples\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"title_length, title_word_count, description_length, description_word_count\",\n          \"description_density, title_desc_ratio\",\n          \"formatted_work_type, formatted_experience_level, company_size via one-hot encoding\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Purpose\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"Measure how much information appears in the title and description\",\n          \"Capture how dense or balanced the posting text is\",\n          \"Convert categorical variables into model-ready numeric variables\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "          Feature Group                                           Examples  \\\n",
              "0           Text length  title_length, title_word_count, description_le...   \n",
              "1        Text structure              description_density, title_desc_ratio   \n",
              "2   Salary transparency       salary_midpoint, salary_log, has_salary_info   \n",
              "3         Role keywords  is_senior_role, is_entry_role, is_software_rol...   \n",
              "4          Interactions  salary_description_interaction, salary_density...   \n",
              "5  Categorical encoding  formatted_work_type, formatted_experience_leve...   \n",
              "6    Clustering feature  cluster dummy variables created from K-Means l...   \n",
              "\n",
              "                                             Purpose  \n",
              "0  Measure how much information appears in the ti...  \n",
              "1  Capture how dense or balanced the posting text is  \n",
              "2     Represent salary availability and salary scale  \n",
              "3  Extract useful meaning from job titles and rol...  \n",
              "4  Capture combinations of features that may matt...  \n",
              "5  Convert categorical variables into model-ready...  \n",
              "6  Use unsupervised segmentation as additional mo...  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-59f65e28-5174-4987-9543-f91f96fb57bd\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Feature Group</th>\n",
              "      <th>Examples</th>\n",
              "      <th>Purpose</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Text length</td>\n",
              "      <td>title_length, title_word_count, description_le...</td>\n",
              "      <td>Measure how much information appears in the ti...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Text structure</td>\n",
              "      <td>description_density, title_desc_ratio</td>\n",
              "      <td>Capture how dense or balanced the posting text is</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Salary transparency</td>\n",
              "      <td>salary_midpoint, salary_log, has_salary_info</td>\n",
              "      <td>Represent salary availability and salary scale</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Role keywords</td>\n",
              "      <td>is_senior_role, is_entry_role, is_software_rol...</td>\n",
              "      <td>Extract useful meaning from job titles and rol...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Interactions</td>\n",
              "      <td>salary_description_interaction, salary_density...</td>\n",
              "      <td>Capture combinations of features that may matt...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Categorical encoding</td>\n",
              "      <td>formatted_work_type, formatted_experience_leve...</td>\n",
              "      <td>Convert categorical variables into model-ready...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Clustering feature</td>\n",
              "      <td>cluster dummy variables created from K-Means l...</td>\n",
              "      <td>Use unsupervised segmentation as additional mo...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-59f65e28-5174-4987-9543-f91f96fb57bd')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-59f65e28-5174-4987-9543-f91f96fb57bd button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-59f65e28-5174-4987-9543-f91f96fb57bd');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "  <div id=\"id_a44bf8c7-c339-4bfe-8847-cad53ea454a1\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('feature_engineering_summary')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_a44bf8c7-c339-4bfe-8847-cad53ea454a1 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('feature_engineering_summary');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "feature_engineering_summary",
              "summary": "{\n  \"name\": \"feature_engineering_summary\",\n  \"rows\": 7,\n  \"fields\": [\n    {\n      \"column\": \"Feature Group\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"Text length\",\n          \"Text structure\",\n          \"Categorical encoding\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Examples\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"title_length, title_word_count, description_length, description_word_count\",\n          \"description_density, title_desc_ratio\",\n          \"formatted_work_type, formatted_experience_level, company_size via one-hot encoding\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Purpose\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"Measure how much information appears in the title and description\",\n          \"Capture how dense or balanced the posting text is\",\n          \"Convert categorical variables into model-ready numeric variables\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l-WXLrFkVdYi"
      },
      "source": [
        "### New Features\n",
        "\n",
        "We recommend reading the resources uploaded to the forum."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Vq9Nh-D1Vdh6"
      },
      "source": [
        "### **Applying Clustering (Unsupervised Learning):**\n",
        "\n",
        "* Run at least one clustering algorithm; K-Means, DBSCAN, etc.\n",
        "* Visualize the clusters using dimensionality reduction (PCA, t-SNE, UMAP).\n",
        "* Interpret the clusters: What makes them distinct? Are they useful?\n",
        "* Use the results to create new features (e.g., cluster ID, distance to centroid, cluster probabilities).\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Clustering: Segmenting Job Postings\n",
        "\n",
        "K-Means clustering is used to identify groups of similar job postings based on a focused set of engineered numeric features.\n",
        "\n",
        "To avoid target leakage, the clustering features do **not** include `views`, `log_views`, or any target-related variable. The clustering preprocessor is fit only on the training data, and the test set is transformed using the training-fitted preprocessor.\n",
        "\n",
        "The resulting cluster labels are later added as engineered features to the supervised regression and classification datasets."
      ],
      "metadata": {
        "id": "vH_A4MzsgQMw"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tBUK491Mt3MC",
        "outputId": "91a28c54-2937-4c9d-a42f-67e52b5dfa84"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Clustering features:\n",
            "['title_word_count', 'description_word_count', 'salary_log', 'description_density', 'has_salary_info', 'is_senior_role', 'is_entry_role', 'is_software_role', 'is_data_role', 'is_manager_role', 'is_sales_role', 'is_marketing_role']\n",
            "Number of clustering features: 12\n",
            "Training cluster matrix shape: (23657, 12)\n",
            "Test cluster matrix shape: (5915, 12)\n"
          ]
        }
      ],
      "source": [
        "# 4.4 Prepare Data for Clustering\n",
        "\n",
        "\n",
        "cluster_features = [\n",
        "    \"title_word_count\",\n",
        "    \"description_word_count\",\n",
        "    \"salary_log\",\n",
        "    \"description_density\",\n",
        "    \"has_salary_info\",\n",
        "    \"is_senior_role\",\n",
        "    \"is_entry_role\",\n",
        "    \"is_software_role\",\n",
        "    \"is_data_role\",\n",
        "    \"is_manager_role\",\n",
        "    \"is_sales_role\",\n",
        "    \"is_marketing_role\"\n",
        "]\n",
        "\n",
        "cluster_features = [\n",
        "    col for col in cluster_features\n",
        "    if col in X_train.columns\n",
        "]\n",
        "\n",
        "for bad_term in [\"view\", \"target\", \"engagement\", \"cluster\"]:\n",
        "    bad_cols = [col for col in cluster_features if bad_term in col.lower()]\n",
        "    if bad_cols:\n",
        "        raise ValueError(f\"Potential leakage in cluster_features: {bad_cols}\")\n",
        "\n",
        "print(\"Clustering features:\")\n",
        "print(cluster_features)\n",
        "print(\"Number of clustering features:\", len(cluster_features))\n",
        "\n",
        "cluster_preprocessor = Pipeline(steps=[\n",
        "    (\"imputer\", SimpleImputer(strategy=\"median\")),\n",
        "    (\"scaler\", StandardScaler())\n",
        "])\n",
        "\n",
        "X_train_cluster_matrix = cluster_preprocessor.fit_transform(\n",
        "    X_train[cluster_features]\n",
        ")\n",
        "\n",
        "X_test_cluster_matrix = cluster_preprocessor.transform(\n",
        "    X_test[cluster_features]\n",
        ")\n",
        "\n",
        "print(\"Training cluster matrix shape:\", X_train_cluster_matrix.shape)\n",
        "print(\"Test cluster matrix shape:\", X_test_cluster_matrix.shape)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 4.4 Elbow Method\n",
        "\n",
        "The elbow method compares K-Means inertia across different values of `K`. Inertia measures within-cluster compactness. Lower inertia is better, but it naturally decreases as `K` increases, so this method is used as a diagnostic rather than the final decision rule."
      ],
      "metadata": {
        "id": "ppYGIT2ZoubT"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 822
        },
        "id": "AIaI76aM7yVO",
        "outputId": "e39b8a33-8a09-4fce-c367-d1d43b7b7450"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "    k        inertia\n",
              "0   2  255430.795012\n",
              "1   3  230467.247414\n",
              "2   4  208572.028434\n",
              "3   5  188061.927131\n",
              "4   6  167260.590163\n",
              "5   7  146696.960121\n",
              "6   8  127659.250993\n",
              "7   9  113143.090976\n",
              "8  10   98508.026988"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-db41f2b1-c261-46bf-a960-9ab36fc0610c\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>k</th>\n",
              "      <th>inertia</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2</td>\n",
              "      <td>255430.795012</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3</td>\n",
              "      <td>230467.247414</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4</td>\n",
              "      <td>208572.028434</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5</td>\n",
              "      <td>188061.927131</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>6</td>\n",
              "      <td>167260.590163</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>7</td>\n",
              "      <td>146696.960121</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>8</td>\n",
              "      <td>127659.250993</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>9</td>\n",
              "      <td>113143.090976</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>10</td>\n",
              "      <td>98508.026988</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-db41f2b1-c261-46bf-a960-9ab36fc0610c')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-db41f2b1-c261-46bf-a960-9ab36fc0610c button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-db41f2b1-c261-46bf-a960-9ab36fc0610c');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "  <div id=\"id_13a4d80f-1d8a-4976-88ba-f831805a43d3\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('elbow_df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_13a4d80f-1d8a-4976-88ba-f831805a43d3 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('elbow_df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "elbow_df",
              "summary": "{\n  \"name\": \"elbow_df\",\n  \"rows\": 9,\n  \"fields\": [\n    {\n      \"column\": \"k\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 2,\n        \"max\": 10,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          9,\n          3,\n          7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"inertia\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 54143.43338953012,\n        \"min\": 98508.02698772853,\n        \"max\": 255430.79501248678,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          113143.09097611804,\n          230467.247413597,\n          146696.96012088703\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 700x500 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAArIAAAHqCAYAAAD4TK2HAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjAsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvlHJYcgAAAAlwSFlzAAAPYQAAD2EBqD+naQAAkENJREFUeJzs3XlYVGX/BvD7DNsgsiqrAoKaivsGoqhZKBqZpGUumZrmEqhIqdni0mZZ5p5lb6/6q1wrfd2NcMEFNxQV9wWXlAEVmUGUbeb5/UHMOOECBnMGuD/XxfU25zxzznfuGXq/PZx5jiSEECAiIiIiqmAUchdARERERPQ02MgSERERUYXERpaIiIiIKiQ2skRERERUIbGRJSIiIqIKiY0sEREREVVIbGSJiIiIqEJiI0tEREREFRIbWSIiIiKqkNjIEpFsJEnCtGnT9I+nTZsGSZJw69Yt+YoyU3Xq1MGLL75Y7ufZuXMnJEnCzp07nzj20KFDaN++Pezs7CBJEpKSksq9PiKiB7GRJaIytXTpUkiS9Mif/fv3y13iU6tTpw4kSUJoaOhD9//www/613n48OFSH//UqVOYNm0aLl++/C8rLX/5+fl49dVXkZGRgdmzZ+Onn36Cr69vuZ2vqMH+9ddfjbbn5eXhxRdfhEKhwH//+9/HPleSJPz8888PHdOhQwdIkoQmTZqUee1EVH4s5S6AiCqnjz/+GH5+fsW216tXT4Zqyo5SqcSOHTugUqng4eFhtO+XX36BUqlETk7OUx371KlTmD59Op599lnUqVOnDKotPxcvXsSVK1fwww8/YPjw4bLUkJ+fj1deeQWbN2/GDz/8gDfffPOx45VKJZYvX47XX3/daPvly5exb98+KJXK8iyXiMoBG1kiKhc9evRAmzZt5C6jzHXo0AGHDh3CqlWrMG7cOP32v/76C7t378bLL7+M3377TcYKTSM9PR0A4OTkVGbHzM7Ohp2dXYnG5ufno2/fvti4cSO+//57DBs27InPeeGFF7B+/XrcunULNWvW1G9fvnw53N3dUb9+fdy5c+ep6yci0+OlBURkdm7duoW+ffvCwcEBNWrUwLhx44rNchYUFOCTTz5B3bp1YWNjgzp16uD9999Hbm6ufkxMTAxq1KgBIYR+25gxYyBJEubNm6fflpaWBkmSsGjRoifWplQq0bt3byxfvtxo+4oVK+Ds7IywsLCHPu/MmTN45ZVX4OLiAqVSiTZt2mD9+vX6/UuXLsWrr74KAOjSpYv+T+H/vFZ1z549CAwMhFKphL+/P/7v//6v2LkuXbqEV199FS4uLqhWrRratWuHTZs2FRv3119/ISIiAnZ2dnBzc8P48eON8nuUIUOGoHPnzgCAV199FZIk4dlnn9Xv3759Ozp27Ag7Ozs4OTmhV69eOH36tNExiq6HPnXqFAYMGABnZ2eEhIQ88dxA4Xvfr18//O9//8OiRYvw1ltvleh5vXr1go2NDdasWWO0ffny5ejbty8sLCwe+ryff/4ZrVu3hq2tLVxcXNCvXz9cu3bNaMzu3bvx6quvwsfHBzY2NvD29sb48eNx//59o3FDhgxB9erVcf36dURERKB69epwdXXFu+++C61WazR25cqVaN26Nezt7eHg4ICmTZti7ty5JXqtRFUFG1kiKhdqtRq3bt0y+rl9+3aJntu3b1/k5ORgxowZeOGFFzBv3jyMGDHCaMzw4cMxZcoUtGrVCrNnz0bnzp0xY8YM9OvXTz+mY8eOyMjIwMmTJ/Xbdu/eDYVCgd27dxttA4BOnTqVqL4BAwbg4MGDuHjxon7b8uXL8corr8DKyqrY+JMnT6Jdu3Y4ffo03nvvPcyaNQt2dnaIiIjA2rVr9eceO3YsAOD999/HTz/9hJ9++gmNGjXSH+fChQt45ZVX0LVrV8yaNQvOzs4YMmSI0etLS0tD+/btsW3bNrz99tv47LPPkJOTg5deekl/LgC4f/8+nn/+eWzbtg1RUVH44IMPsHv3bkycOPGJr3/kyJF4//33AQBjx47FTz/9hA8++AAA8OeffyIsLAzp6emYNm0aYmJisG/fPnTo0OGh1/6++uqruHfvHj7//PMSNaQFBQXo378/1q5di4ULF2LkyJFPfE6RatWqoVevXlixYoV+27Fjx3Dy5EkMGDDgoc/57LPP8MYbb6B+/fr45ptvEB0djbi4OHTq1AmZmZn6cWvWrMG9e/cwevRozJ8/H2FhYZg/fz7eeOONYsfUarUICwtDjRo18PXXX6Nz586YNWsWFi9erB8TGxuL/v37w9nZGV9++SW++OILPPvss9i7d2+JXy9RlSCIiMrQkiVLBICH/tjY2BiNBSCmTp2qfzx16lQBQLz00ktG495++20BQBw7dkwIIURSUpIAIIYPH2407t133xUAxPbt24UQQqSnpwsA4ttvvxVCCJGZmSkUCoV49dVXhbu7u/55Y8eOFS4uLkKn0z32tfn6+orw8HBRUFAgPDw8xCeffCKEEOLUqVMCgNi1a5f+9R86dEj/vOeff140bdpU5OTk6LfpdDrRvn17Ub9+ff22NWvWCABix44dDz03ABEfH6/flp6eLmxsbMQ777yj3xYdHS0AiN27d+u3ZWVlCT8/P1GnTh2h1WqFEELMmTNHABCrV6/Wj8vOzhb16tV7ZA0P2rFjhwAg1qxZY7S9RYsWws3NTdy+fVu/7dixY0KhUIg33nhDv63ove7fv/9jz/PP8xXlsHDhwhI975+1bty4UUiSJK5evSqEEGLChAnC399fCCFE586dRePGjfXPu3z5srCwsBCfffaZ0fFOnDghLC0tjbbfu3ev2HlnzJghJEkSV65c0W8bPHiwACA+/vhjo7EtW7YUrVu31j8eN26ccHBwEAUFBSV+nURVEWdkiahcLFy4ELGxsUY/W7ZsKdFzIyMjjR6PGTMGALB582aj/42JiTEa98477wCA/s/orq6uaNiwIeLj4wEAe/fuhYWFBSZMmIC0tDScP38eQOGMbEhICCRJKlF9FhYW6Nu3r35m75dffoG3tzc6duxYbGxGRga2b9+Ovn37Iisry2h2OiwsDOfPn8f169dLdN6AgACjc7i6uqJBgwa4dOmSftvmzZsRGBho9Gf66tWrY8SIEbh8+TJOnTqlH+fp6YlXXnlFP65atWrFZr5LIzU1FUlJSRgyZAhcXFz025s1a4auXbvq37cHjRo1qlTnSEtLg6Wl5UO/SFgS3bp1g4uLC1auXAkhBFauXIn+/fs/dOzvv/8OnU6Hvn37Gv1lwcPDA/Xr18eOHTv0Y21tbfX/nJ2djVu3bqF9+/YQQuDo0aPFjv3P192xY0ej99HJyQnZ2dmIjY19qtdJVFXwy15EVC4CAwOf+ste9evXN3pct25dKBQK/Z+mr1y5AoVCUWwFBA8PDzg5OeHKlSv6bR07dtQ3ULt370abNm3Qpk0buLi4YPfu3XB3d8exY8ce+aflRxkwYADmzZuHY8eOYfny5ejXr99DG+ELFy5ACIGPPvoIH3300UOPlZ6ejlq1aj3xnD4+PsW2OTs7G31B6cqVKwgKCio2rugShStXrqBJkya4cuUK6tWrV6zmBg0aPLGORynK/WHHaNSoEbZt21bsC12lbUhnzpyJOXPm4JVXXsEff/yBDh066PepVCqjsY6OjkYNJgBYWVnh1VdfxfLlyxEYGIhr16498r0/f/48hBDFPo8PHqvI1atXMWXKFKxfv77YF8bUarXRY6VSCVdXV6Nt/3wf3377baxevRo9evRArVq10K1bN/Tt2xfdu3d/aC1EVRUbWSIye4+aKS3JDGpISAh++OEHXLp0Cbt370bHjh0hSRJCQkKwe/dueHl5QafTPXQ29XGCgoJQt25dREdHIyUl5ZHNkE6nAwC8++67j/wiWEmXJHvUl5HEA19mq2j+2Wg+iaenJ2JjYxESEoLw8HDs2rULzZs31+970JIlSzBkyJBixxgwYAC+++47TJs2Dc2bN0dAQMBDz6XT6SBJErZs2fLQ7KtXrw6g8JrXrl27IiMjA5MmTULDhg1hZ2eH69evY8iQIfrPQJFHvY8PcnNzQ1JSErZt24YtW7Zgy5YtWLJkCd544w0sW7bsic8nqirYyBKR2Tl//rzRTN2FCxeg0+n0a6v6+vpCp9Ph/PnzRl+GSktLQ2ZmptHC/EUNamxsLA4dOoT33nsPQOGXqxYtWgQvLy/Y2dmhdevWpa6zf//++PTTT9GoUSO0aNHioWP8/f0BFM7ePepGCkVKemnD4/j6+uLs2bPFtp85c0a/v+h/k5OTIYQwOu/Dnluacz/qGGfOnEHNmjVLvLzW4/j7+2Pbtm3o3LkzwsLCsHv3btSvX7/Yn+EbN2780OeHhITAx8cHO3fuxJdffvnI89StWxdCCPj5+eGZZ5555LgTJ07g3LlzWLZsmdGXu/7tZQHW1tbo2bMnevbsCZ1Oh7fffhvff/89Pvroowq/HjNRWeE1skRkdhYuXGj0eP78+QAK16YFCtcDBYA5c+YYjfvmm28AAOHh4fptfn5+qFWrFmbPno38/Hz9n6I7duyIixcv4tdff0W7du1gaVn6/64fPnw4pk6dilmzZj1yjJubG5599ll8//33SE1NLbb/5s2b+n8uavIe/DZ8ab3wwgs4ePAgEhIS9Nuys7OxePFi1KlTRz/7+MILL+DGjRtGd8q6d++e0TfnS8vT0xMtWrTAsmXLjF5DcnIy/vjjD/37VhaaNm2KTZs24e7du+jatSuuX7+O0NBQo59/ztAWKVp+berUqRg0aNAjz9G7d29YWFhg+vTpxWa9hRD6VTiKZlgfHCOE+FdLZf1zhQ+FQoFmzZoBQImWSCOqKjgjS0TlYsuWLfpZwAe1b99eP0v5KCkpKXjppZfQvXt3JCQk4Oeff8aAAQP0f0Ju3rw5Bg8ejMWLFyMzMxOdO3fGwYMHsWzZMkRERKBLly5Gx+vYsSNWrlyJpk2bwtnZGQDQqlUr2NnZ4dy5c6W+PraIr68vpk2b9sRxCxcuREhICJo2bYq33noL/v7+SEtLQ0JCAv766y8cO3YMANCiRQtYWFjgyy+/hFqtho2NDZ577jm4ubmVuKb33nsPK1asQI8ePTB27Fi4uLhg2bJlSElJwW+//QaFonD+4q233sKCBQvwxhtvIDExEZ6envjpp59QrVq1p8qiyFdffYUePXogODgYw4YNw/379zF//nw4OjqWKKvSCA4Oxu+//46ePXuia9eu2L17N2rUqFGi5/bq1Qu9evV67Ji6devi008/xeTJk3H58mVERETA3t4eKSkpWLt2LUaMGIF3330XDRs2RN26dfHuu+/i+vXrcHBwwG+//favbq4wfPhwZGRk4LnnnkPt2rVx5coVzJ8/Hy1atDD6KwRRlSfTaglEVEk9bvktAGLJkiX6sXjE8lunTp0Sr7zyirC3txfOzs4iKipK3L9/3+g8+fn5Yvr06cLPz09YWVkJb29vMXnyZKMlroosXLhQABCjR4822h4aGioAiLi4uBK9tqLlt0ry+h9cfksIIS5evCjeeOMN4eHhIaysrEStWrXEiy++KH799VejcT/88IPw9/cXFhYWRstgPercnTt3Fp07dy52rldeeUU4OTkJpVIpAgMDxcaNG4s998qVK+Kll14S1apVEzVr1hTjxo0TW7du/VfLbwkhxJ9//ik6dOggbG1thYODg+jZs6c4deqU0Zii9/rmzZuPPU9Jzrdq1SqhUChE27ZthUajKdVzH/TP5beK/PbbbyIkJETY2dkJOzs70bBhQxEZGSnOnj2rH3Pq1CkRGhoqqlevLmrWrCneeustcezYsWKf+cGDBws7O7ti5yjKo8ivv/4qunXrJtzc3IS1tbXw8fERI0eOFKmpqY99DURVjSREBf6WABERERFVWbxGloiIiIgqJDayRERERFQhsZElIiIiogqJjSwRERERVUhsZImIiIioQmIjS0REREQVEm+IYEI6nQ43btyAvb19mdyKkoiIiKiyEUIgKysLXl5e+pu4PAobWRO6ceMGvL295S6DiIiIyOxdu3YNtWvXfuwYNrImZG9vD6DwjXFwcCjXc+l0Oty8eROurq5P/K+Zyo5ZFGIOBszCgFkYMAsDZlGIORiYMguNRgNvb2993/Q4bGRNqOhyAgcHB5M0sjk5OXBwcOAvH7MAwBwexCwMmIUBszBgFoWYg4EcWZTkMsyq/a4QERERUYUlayM7Y8YMtG3bFvb29nBzc0NERATOnj1bbFxCQgKee+452NnZwcHBAZ06dcL9+/f1+zMyMjBw4EA4ODjAyckJw4YNw927d42Ocfz4cXTs2BFKpRLe3t6YOXNmsfOsWbMGDRs2hFKpRNOmTbF582aj/UIITJkyBZ6enrC1tUVoaCjOnz9fRmkQERERUWnI2sju2rULkZGR2L9/P2JjY5Gfn49u3bohOztbPyYhIQHdu3dHt27dcPDgQRw6dAhRUVFG09oDBw7EyZMnERsbi40bNyI+Ph4jRozQ79doNOjWrRt8fX2RmJiIr776CtOmTcPixYv1Y/bt24f+/ftj2LBhOHr0KCIiIhAREYHk5GT9mJkzZ2LevHn47rvvcODAAdjZ2SEsLAw5OTnlnBQRERERFSPMSHp6ugAgdu3apd8WFBQkPvzww0c+59SpUwKAOHTokH7bli1bhCRJ4vr160IIIb799lvh7OwscnNz9WMmTZokGjRooH/ct29fER4ebnTsoKAgMXLkSCGEEDqdTnh4eIivvvpKvz8zM1PY2NiIFStWlOj1qdVqAUCo1eoSjf83tFqtSE1NFVqtttzPZe6YRSHmYMAsDJiFAbMwYBaFmIOBKbMoTb9kVl/2UqvVAAAXFxcAQHp6Og4cOICBAweiffv2uHjxIho2bIjPPvsMISEhAApnbJ2cnNCmTRv9cUJDQ6FQKHDgwAG8/PLLSEhIQKdOnWBtba0fExYWhi+//BJ37tyBs7MzEhISEBMTY1RPWFgY1q1bBwBISUmBSqVCaGiofr+joyOCgoKQkJCAfv36FXs9ubm5yM3N1T/WaDQACi+Y1ul0/yaqJ9LpdBBClPt5KgJmUYg5GDALA2ZhwCwMmEUh5mBgyixKcw6zaWR1Oh2io6PRoUMHNGnSBABw6dIlAMC0adPw9ddfo0WLFvi///s/PP/880hOTkb9+vWhUqng5uZmdCxLS0u4uLhApVIBAFQqFfz8/IzGuLu76/c5OztDpVLptz045sFjPPi8h435pxkzZmD69OnFtt+8ebPcL0fQ6XRQq9UQQvCblswCAHN4ELMwYBYGzMKAWRRiDgamzCIrK6vEY82mkY2MjERycjL27Nmj31bUkY8cORJDhw4FALRs2RJxcXH473//ixkzZshSa0lNnjzZaJa3aF00V1dXkyy/JUkS174DsyjCHAyYhQGzMGAWBsyiEHMwMGUWSqWyxGPNopGNiorSf0nrwTs4eHp6AgACAgKMxjdq1AhXr14FAHh4eCA9Pd1of0FBATIyMuDh4aEfk5aWZjSm6PGTxjy4v2hbUV1Fj1u0aPHQ12VjYwMbG5ti2xUKhUl+ISRJMtm5zB2zKMQcDJiFAbMwYBYGzKIQczAwVRalOb6s74oQAlFRUVi7di22b99e7M//derUgZeXV7Eluc6dOwdfX18AQHBwMDIzM5GYmKjfv337duh0OgQFBenHxMfHIz8/Xz8mNjYWDRo0gLOzs35MXFyc0XliY2MRHBwMAPDz84OHh4fRGI1GgwMHDujHEBEREZHpyNrIRkZG4ueff8by5cthb28PlUoFlUqlXyNWkiRMmDAB8+bNw6+//ooLFy7go48+wpkzZzBs2DAAhbOz3bt3x1tvvYWDBw9i7969iIqKQr9+/eDl5QUAGDBgAKytrTFs2DCcPHkSq1atwty5c43+7D9u3Dhs3boVs2bNwpkzZzBt2jQcPnwYUVFR+lqio6Px6aefYv369Thx4gTeeOMNeHl5ISIiwrTBEREREZG8y28BeOjPkiVLjMbNmDFD1K5dW1SrVk0EBweL3bt3G+2/ffu26N+/v6hevbpwcHAQQ4cOFVlZWUZjjh07JkJCQoSNjY2oVauW+OKLL4rVs3r1avHMM88Ia2tr0bhxY7Fp0yaj/TqdTnz00UfC3d1d2NjYiOeff16cPXu2xK+Xy2/Jg1kUYg4GzMKAWRgwCwNmUYg5GJjr8luSEELI10ZXLRqNBo6OjlCr1eX6ZS+tTuDApVu48NdN1KvtiiD/mrBQPPl+xZWVTqdDeno63NzcqvQ1TszBgFkYMAsDZmHALAoxBwNTZlGafsksvuxFZWdrciqmbziFVHXR8l4p8HRUYmrPAHRv4vnY5xIRERFVJFX7Py8qma3JqRj985EHmthCKnUORv98BFuTU2WqjIiIiKjssZGtJLQ6gekbTuFh14kUbZu+4RS0Ol5JQkRERJUDG9lK4mBKRrGZ2AcJAKnqHBxMyTBdUURERETliI1sJZGeVbJb3pZ0HBEREZG5YyNbSbjZl+x2biUdR0RERGTu2MhWEoF+LvB0VOJxi2xVt7FE2zrOJquJiIiIqDyxka0kLBQSpvYMAIBHNrN3cwsw4dfjyCvQma4wIiIionLCRrYS6d7EE4tebwUPR+PLBzwdlRjUzheWCglrj17H4P8ehPp+vkxVEhEREZUN3hChkunexBNdAzweemevrgHuGP1zIhIu3car3+3DkqGBqOVkK3fJRERERE+FM7KVkIVCQjv/GujW0AXt/Gvob0/b6RlXrB4VDHcHG5xLu4uXF+5F8nW1zNUSERERPR02slVMYy9HrH27Axq42yM9KxevfZ+AnWfT5S6LiIiIqNTYyFZBXk62WDM6GB3q1UB2nhbDlh3GyoNX5S6LiIiIqFTYyFZRDkorLBkSiN6takGrE3jv9xOY9cdZCMFb2BIREVHFwEa2CrO2VGDWq80x9vn6AID52y/gndXHuDwXERERVQhsZKs4SZIQ0/UZzOzTDBYKCb9zeS4iIiKqINjIEgCgb1tv/HdIW9hZW+iX57qeeV/usoiIiIgeiY0s6XV+yPJcJ29weS4iIiIyT2xkycg/l+fq+x2X5yIiIiLzxEaWivFyssXqUcFoX9ewPNeqQ1yei4iIiMwLG1l6KEdbKywdaliea9JvXJ6LiIiIzAsbWXok/fJcz9UDwOW5iIiIyLywkaXHkiQJMd0a4Ms+TfXLcw1ZwuW5iIiISH5sZKlEXmvro1+ea9/FwuW5bnB5LiIiIpIRG1kqsaLludzs/16e61suz0VERETyYSNLpdLYyxFrIzvgGffqSNMULs+169xNucsiIiKiKoiNLJVaLSdbrBnVHsH+hctzvbn0EJfnIiIiIpNjI0tPxdHWCsveDETvloblub7h8lxERERkQmxk6alZWyowq29zjPl7ea55XJ6LiIiITIiNLP0rkiThnW4N8EVv4+W5NDlcnouIiIjKFxtZKhP9An3w4+A2huW5FiVweS4iIiIqV2xkqcw828ANq0YWLs91Ni2Ly3MRERFRuWIjS2WqSS0uz0VERESmwUaWyhyX5yIiIiJTYCNL5aJoea6XuTwXERERlRNZG9kZM2agbdu2sLe3h5ubGyIiInD27NmHjhVCoEePHpAkCevWrTPad/XqVYSHh6NatWpwc3PDhAkTUFBQYDRm586daNWqFWxsbFCvXj0sXbq02DkWLlyIOnXqQKlUIigoCAcPHjTan5OTg8jISNSoUQPVq1dHnz59kJaW9q8yqMysLRX4hstzERERUTmRtZHdtWsXIiMjsX//fsTGxiI/Px/dunVDdnZ2sbFz5syBJEnFtmu1WoSHhyMvLw/79u3DsmXLsHTpUkyZMkU/JiUlBeHh4ejSpQuSkpIQHR2N4cOHY9u2bfoxq1atQkxMDKZOnYojR46gefPmCAsLQ3p6un7M+PHjsWHDBqxZswa7du3CjRs30Lt37zJOpXIpWp5rBpfnIiIiorImzEh6eroAIHbt2mW0/ejRo6JWrVoiNTVVABBr167V79u8ebNQKBRCpVLpty1atEg4ODiI3NxcIYQQEydOFI0bNzY65muvvSbCwsL0jwMDA0VkZKT+sVarFV5eXmLGjBlCCCEyMzOFlZWVWLNmjX7M6dOnBQCRkJBQotenVqsFAKFWq0s0/t/QarUiNTVVaLXacj9XSW0/kyYCPtoifCdtFN2+2SWu37lnkvOaYxZyYA4GzMKAWRgwCwNmUYg5GJgyi9L0S2Z1jaxaXbhUk4uLi37bvXv3MGDAACxcuBAeHh7FnpOQkICmTZvC3d1dvy0sLAwajQYnT57UjwkNDTV6XlhYGBISEgAAeXl5SExMNBqjUCgQGhqqH5OYmIj8/HyjMQ0bNoSPj49+DD1el7+X53Ll8lxERERUBizlLqCITqdDdHQ0OnTogCZNmui3jx8/Hu3bt0evXr0e+jyVSmXUxALQP1apVI8do9FocP/+fdy5cwdarfahY86cOaM/hrW1NZycnIqNKTrPP+Xm5iI3N1f/WKPR6F+rTle+14nqdDoIIcr9PKUV4GmP30cF481lh3E+/S76fpeAhQNaotMzruV2TnPNwtSYgwGzMGAWBszCgFkUYg4GpsyiNOcwm0Y2MjISycnJ2LNnj37b+vXrsX37dhw9elTGyp7ejBkzMH369GLbb968iZycnHI9t06ng1qthhACCoVZTbzDCsC3veti0saLOPLXXQxbdhjvhfqiZ+Oa5XI+c87ClJiDAbMwYBYGzMKAWRRiDgamzCIrK6vEY82ikY2KisLGjRsRHx+P2rVr67dv374dFy9eLDYL2qdPH3Ts2BE7d+6Eh4dHsdUFilYSKLoUwcPDo9jqAmlpaXBwcICtrS0sLCxgYWHx0DEPHiMvLw+ZmZlG9Tw45p8mT56MmJgY/WONRgNvb2+4urrCwcGhBMk8PZ1OB0mS4Orqapa/fG4Afhnhjvd+T8b/km7gs9gr0BRYIjq0/kO/1PdvmHsWpsIcDJiFAbMwYBYGzKIQczAwZRZKpbLEY2VtZIUQGDNmDNauXYudO3fCz8/PaP97772H4cOHG21r2rQpZs+ejZ49ewIAgoOD8dlnnyE9PR1ubm4AgNjYWDg4OCAgIEA/ZvPmzUbHiY2NRXBwMADA2toarVu3RlxcHCIiIgAUvmFxcXGIiooCALRu3RpWVlaIi4tDnz59AABnz57F1atX9cf5JxsbG9jY2BTbrlAoTPILIUmSyc71NGytFZjzWgt4O1fDgh0XMH/HRVxX5+CL3s1gbVm2NZt7FqbCHAyYhQGzMGAWBsyiEHMwMFUWpTm+rI1sZGQkli9fjv/973+wt7fXX2vq6OgIW1tbeHh4PHS208fHR9/0duvWDQEBARg0aBBmzpwJlUqFDz/8EJGRkfomctSoUViwYAEmTpyIN998E9u3b8fq1auxadMm/TFjYmIwePBgtGnTBoGBgZgzZw6ys7MxdOhQfU3Dhg1DTEwMXFxc4ODggDFjxiA4OBjt2rUr76gqLUmS8G5YA9RytsWH65Lx+5HrSNPkYNHrreGgtJK7PCIiIjJjsjayixYtAgA8++yzRtuXLFmCIUOGlOgYFhYW2LhxI0aPHo3g4GDY2dlh8ODB+Pjjj/Vj/Pz8sGnTJowfPx5z585F7dq18Z///AdhYWH6Ma+99hpu3ryJKVOmQKVSoUWLFti6davRF8Bmz54NhUKBPn36IDc3F2FhYfj222+fPgDS6x/oAw9HJSJ/OYK9F27j1UUJWDK0LbycbOUujYiIiMyUJATvGWoqGo0Gjo6OUKvVJrlGtuhyi4r055Dk62oMXXoIN7Ny4e5ggyVDAhHg9e+yqqhZlDXmYMAsDJiFAbMwYBaFmIOBKbMoTb9Utd8VMjtNajli7dvtUd+tOtI0uej7fQLiz92UuywiIiIyQ2xkyezUdq6GX0e1Rzt/F9zNLcCbSw9h9eFrcpdFREREZoaNLJklx2pWWPZmIHq18EKBTmDir8fxTew58EoYIiIiKsJGlsyWjaUF5rzWApFd6gIA5sWdx7trjiOvgHdYISIiIjayZOYkScKEsIb4/OWmsFBI+O3IX3hz6SFocvLlLo2IiIhkxkaWKoQBQT74zxttUM3aAnsu3ELf7xKQqr4vd1lEREQkIzayVGF0aeiG1SOD4WpvgzOqLLy8cB9O3dDIXRYRERHJhI0sVShFy3PVc6sOlSaHy3MRERFVYWxkqcKp7VwNv3F5LiIioiqPjSxVSA9bnms2l+ciIiKqUtjIUoVlY2mB2X0Ny3PN5fJcREREVQobWarQFIrHL8+l1Qnsv3Qbf5zJwP5Lt6HVccaWiIiosrCUuwCisjAgyAeejkpELj+iX55rcHtfzIu7gFR1zt+jUuDpqMTUngHo3sRT1nqJiIjo3+OMLFUa/1yea/LvyQ80sYVU6hyM/vkItianylQlERERlRU2slSpNKnliDUjg2GhkB66v+jCgukbTvEyAyIiogqOjSxVOqnqnMc2qeLvMQdTMkxXFBEREZU5NrJU6aRn5Tx5UCnGERERkXliI0uVjpu9skzHERERkXliI0uVTqCfCzwdlXj4VbKFnKpZIdDPxWQ1ERERUdljI0uVjoVCwtSeAQDwyGY2814+Ptl4ijdPICIiqsDYyFKl1L2JJxa93goejsaXD3g6KtEtwB0AsHTfZby2OAE3Mu/LUSIRERH9S7whAlVa3Zt4omuABw5cuoULf91EvdquCPKvCQuFhNhTaYhZnYSjVzPx4vw9mNuvBTrWd5W7ZCIiIioFzshSpWahkNDOvwa6NXRBO/8a+vVluwa4Y9OYjmjs5YCM7Dy88d+DmPvneei4tiwREVGFwUaWqiyfGtXw2+j26B/oDSGA2X+ew5Clh5CRnSd3aURERFQCbGSpSlNaWWBG72b4+tXmUFopEH/uJl6ctxtHr96RuzQiIiJ6AjayRABeaV0ba9/uAL+adrihzkHf7xOwdG8KhOClBkREROaKjSzR3xp5OuB/UR3Qo4kH8rUC0zacwtiVSbibWyB3aURERPQQbGSJHuCgtMK3A1vhw/BGsFRI2HDsBnot2INzaVlyl0ZERET/wEaW6B8kScLwjv5YOaId3B1scPFmNnot2Iv/JV2XuzQiIiJ6ABtZokdoU8cFm8Z2RId6NXA/X4txK5Pw0bpk5BZo5S6NiIiIwEaW6LFqVrfB/70ZhDHP1QMA/LT/Cvp+l4C/7tyTuTIiIiJiI0v0BBYKCe90a4AlQ9vCqZoVjv2lRvi8PdhxJl3u0oiIiKo0NrJEJdSlgRs2jglB89qOUN/Px9Clh/D1trPQ8m5gREREsmAjS1QKtZ2rYfWoYAxq5wsAWLDjAt747wHcupsrc2VERERVDxtZolKysbTAJxFNMLdfC9haWWDvhdsIn7cbhy9nyF0aERFRlcJGlugp9WpRC+ujOqCuqx3SNLnot3g//rP7Eu8GRkREZCKyNrIzZsxA27ZtYW9vDzc3N0RERODs2bP6/RkZGRgzZgwaNGgAW1tb+Pj4YOzYsVCr1UbHuXr1KsLDw1GtWjW4ublhwoQJKCgwvhvTzp070apVK9jY2KBevXpYunRpsXoWLlyIOnXqQKlUIigoCAcPHjTan5OTg8jISNSoUQPVq1dHnz59kJaWVnaBUIVT390e66NC0LO5Fwp0Ap9uOo23fzmCrJx8uUsjIiKq9GRtZHft2oXIyEjs378fsbGxyM/PR7du3ZCdnQ0AuHHjBm7cuIGvv/4aycnJWLp0KbZu3Yphw4bpj6HVahEeHo68vDzs27cPy5Ytw9KlSzFlyhT9mJSUFISHh6NLly5ISkpCdHQ0hg8fjm3btunHrFq1CjExMZg6dSqOHDmC5s2bIywsDOnphm+mjx8/Hhs2bMCaNWuwa9cu3LhxA7179zZBUmTO7GwsMa9fC0x/qTGsLCRsSVbhpQV7cUalkbs0IiKiyk2YkfT0dAFA7Nq165FjVq9eLaytrUV+fr4QQojNmzcLhUIhVCqVfsyiRYuEg4ODyM3NFUIIMXHiRNG4cWOj47z22msiLCxM/zgwMFBERkbqH2u1WuHl5SVmzJghhBAiMzNTWFlZiTVr1ujHnD59WgAQCQkJJXp9arVaABBqtbpE4/8NrVYrUlNThVarLfdzmTtTZnHkSoYI/vxP4Ttpo2jw4Wax5vC1cj9nSfEzYcAsDJiFAbMwYBaFmIOBKbMoTb9kVtfIFl0y4OLi8tgxDg4OsLS0BAAkJCSgadOmcHd3148JCwuDRqPByZMn9WNCQ0ONjhMWFoaEhAQAQF5eHhITE43GKBQKhIaG6sckJiYiPz/faEzDhg3h4+OjH0PU0scZm8Z2ROdnXJGTr8O7a47hvd+OIyefdwMjIiIqa5ZyF1BEp9MhOjoaHTp0QJMmTR465tatW/jkk08wYsQI/TaVSmXUxALQP1apVI8do9FocP/+fdy5cwdarfahY86cOaM/hrW1NZycnIqNKTrPP+Xm5iI317Ask0aj0b9WnU730OeUFZ1OByFEuZ+nIjB1Fo62lvjxjdZYsOMC5m6/gJWHruHEdTUWDmgJH5dqJqnhYfiZMGAWBszCgFkYMItCzMHAlFmU5hxm08hGRkYiOTkZe/bseeh+jUaD8PBwBAQEYNq0aaYt7inNmDED06dPL7b95s2byMnJKddz63Q6qNVqCCGgUJjVxLvJyZVFv6aO8HOoj6lbU3DyhgYvztuDKWF10Kmuk8lqeBA/EwbMwoBZGDALA2ZRiDkYmDKLrKysEo81i0Y2KioKGzduRHx8PGrXrl1sf1ZWFrp37w57e3usXbsWVlZW+n0eHh7FVhcoWknAw8ND/7//XF0gLS0NDg4OsLW1hYWFBSwsLB465sFj5OXlITMz02hW9sEx/zR58mTExMToH2s0Gnh7e8PV1RUODg5PiuVf0el0kCQJrq6u/OWTMYuebm5oXb8WxqxIwtFrmZi44SJGdvLHO13rw9LCtLXwM2HALAyYhQGzMGAWhZiDgSmzUCqVJR4rayMrhMCYMWOwdu1a7Ny5E35+fsXGaDQahIWFwcbGBuvXry/24oKDg/HZZ58hPT0dbm5uAIDY2Fg4ODggICBAP2bz5s1Gz4uNjUVwcDAAwNraGq1bt0ZcXBwiIiIAFL5hcXFxiIqKAgC0bt0aVlZWiIuLQ58+fQAAZ8+exdWrV/XH+ScbGxvY2NgU265QKEzyCyFJksnOZe7kzKK2ix1WjQzGjC2nsWTvZXwffwlJ1zIxf0BLuNmX/Je1LPAzYcAsDJiFAbMwYBaFmIOBqbIozfFlfVciIyPx888/Y/ny5bC3t4dKpYJKpcL9+/cBFDaxRctx/fjjj9BoNPoxWm3hl2e6deuGgIAADBo0CMeOHcO2bdvw4YcfIjIyUt9Ejho1CpcuXcLEiRNx5swZfPvtt1i9ejXGjx+vryUmJgY//PADli1bhtOnT2P06NHIzs7G0KFDAQCOjo4YNmwYYmJisGPHDiQmJmLo0KEIDg5Gu3btTJwcVTTWlgpM7dkYCwe0gp21BQ6kZCB83h7sv3Rb7tKIiIgqrrJeMqE0ADz0Z8mSJUIIIXbs2PHIMSkpKfrjXL58WfTo0UPY2tqKmjVrinfeeUe/PFeRHTt2iBYtWghra2vh7++vP8eD5s+fL3x8fIS1tbUIDAwU+/fvN9p///598fbbbwtnZ2dRrVo18fLLL4vU1NQSv14uvyUPc8viQnqW6PrNTuE7aaPwn7xJLNp5Qeh0unI/r7nlICdmYcAsDJiFAbMoxBwMzHX5LUkI3k/TVDQaDRwdHfVLiJUnnU6nv9yiqv85xByzuJdXgA/XJuP3o9cBAKGN3DGrb3M42lo94ZlPzxxzkAuzMGAWBszCgFkUYg4GpsyiNP1S1X5XiGRSzdoSs/o2x+cvN4W1hQJ/nk5Dz/l7kHxd/eQnExEREQA2skSykSQJA4J88Nvo9qjtbIurGffQe9E+rDx4FfxDCRER0ZOxkSWSWdPajtg4JgTPN3RDXoEO7/1+AhN+PY77ebwbGBER0eOwkSUyA07VrPHDG20wIawBFBLwa+JfePnbvUi5lS13aURERGaLjSyRmVAoJER2qYefhwehZnVrnFFloef8PdhyIlXu0oiIiMwSG1kiM9O+bk1sGtsRgXVccDe3AKN/OYJPNp5Cvpb3+iYiInoQG1kiM+TuoMQvbwVhZCd/AMCPe1LQf/F+qNQ5MldGRERkPtjIEpkpKwsFJr/QCN8Pag17G0scvnIHL87fjb0XbsldGhERkVlgI0tk5sIae2DDmBA08nTArbt5GPTjASzYfh46HZfoIiKiqo2NLFEFUKemHda+3R5929SGTgBf/3EOw5YdQua9PLlLIyIikg0bWaIKQmllgZmvNMfMPs1gY6nAjrM3ET5vD47/lSl3aURERLJgI0tUwfRt643f324P3xrVcD3zPl5ZlICf9l/h3cCIiKjKYSNLVAE19nLEhjEh6BbgjjytDh+tS8b4VUm4l1cgd2lEREQmw0aWqIJyUFrh+0Gt8cELjWChkLAu6QZ6LdiLC+l35S6NiIjIJNjIElVgkiThrU7+WPFWO7jZ2+B8+l30WrAHG47dkLs0IiKicsdGlqgSCPRzwcaxIQj2r4HsPC3GrDiKaetPIq+g8G5gWp3A/ku38ceZDOy/dBtaLt1FRESVgKXcBRBR2XCzV+KnYYH4JvYcvt15EUv3XUbStUy82qY2Fmy/gFT9XcFS4OmoxNSeAejexFPWmomIiP4NzsgSVSKWFgpM7N4QPw5uAwelJZKuZeKDtckPNLGFVOocjP75CLYmp8pUKRER0b/HRpaoEnq+kTvWR4XAUiE9dH/RhQXTN5ziZQZERFRhsZElqqRS1TkoeEyTKv4eczAlw3RFERERlSE2skSVVHpWzpMHlWIcERGRuWEjS1RJudkry3QcERGRuWEjS1RJBfq5wNNRiYdfJVvIUiHBxc7aZDURERGVJTayRJWUhULC1J4BAPDIZrZAJxCxcC9WHrwKIfilLyIiqljYyBJVYt2beGLR663g4Wh8+YCnoxJf9G6KkHo1cT9fi/d+P4G3fzmCzHt5MlVKRERUerwhAlEl172JJ7oGeODApVu48NdN1KvtiiD/mrBQSOjbxhv/2XMJX207iy3JKiRdy8Ts11qgnX8NucsmIiJ6Is7IElUBFgoJ7fxroFtDF7TzrwGLv9eXVSgkjOhUF7+P7gC/mnZIVeeg/w/78fW2s8jX6mSumoiI6PHYyBIRmtZ2xMYxIXitjTeEABbsuIBXv0vA1dv35C6NiIjokdjIEhEAwM7GEl++0gwLB7TS3972hXm7sfboX3KXRkRE9FBsZInISHgzT2yJ7oTAOi64m1uA8auOIXrlUWTl5MtdGhERkRE2skRUTC0nW6wY0Q4xXZ+BhULCuqQbeGHebhy5ekfu0oiIiPTYyBLRQ1koJIx9vj5Wj2yH2s62uJZxH69+l4D5ceeh1XHNWSIikh8bWSJ6rNa+Ltg8riNeau4FrU5gVuw59P9hP25k3pe7NCIiquLYyBLREzkorTC3Xwt807c57KwtcDAlA93nxGPLiVS5SyMioiqMjSwRlYgkSejdqjY2j+uI5t5O0OQUYPQvRzD59+O4l1cgd3lERFQFsZElolLxrWGHX0cFI7JLXUgSsOLgNbw4fw+Sr6vlLo2IiKoYWRvZGTNmoG3btrC3t4ebmxsiIiJw9uxZozE5OTmIjIxEjRo1UL16dfTp0wdpaWlGY65evYrw8HBUq1YNbm5umDBhAgoKjGeIdu7ciVatWsHGxgb16tXD0qVLi9WzcOFC1KlTB0qlEkFBQTh48GCpayGqCqwsFJgQ1hDLh7eDh4MSl25m4+Vv9+I/uy9Bxy+CERGRicjayO7atQuRkZHYv38/YmNjkZ+fj27duiE7O1s/Zvz48diwYQPWrFmDXbt24caNG+jdu7d+v1arRXh4OPLy8rBv3z4sW7YMS5cuxZQpU/RjUlJSEB4eji5duiApKQnR0dEYPnw4tm3bph+zatUqxMTEYOrUqThy5AiaN2+OsLAwpKenl7gWoqomuG4NbBnXEWGN3ZGvFfh002kMXnIQ6Vk5cpdGRERVgTAj6enpAoDYtWuXEEKIzMxMYWVlJdasWaMfc/r0aQFAJCQkCCGE2Lx5s1AoFEKlUunHLFq0SDg4OIjc3FwhhBATJ04UjRs3NjrXa6+9JsLCwvSPAwMDRWRkpP6xVqsVXl5eYsaMGSWu5UnUarUAINRqdYnG/xtarVakpqYKrVZb7ucyd8yiUHnmoNPpxC/7r4gGH24WvpM2ilYf/yHiTque/ESZ8DNhwCwMmIUBsyjEHAxMmUVp+iWzukZWrS68xs7FxQUAkJiYiPz8fISGhurHNGzYED4+PkhISAAAJCQkoGnTpnB3d9ePCQsLg0ajwcmTJ/VjHjxG0ZiiY+Tl5SExMdFojEKhQGhoqH5MSWohqqokScKAIB9sHBOCRp4OuJ2dhzeXHsa09SeRk6+VuzwiIqqkLOUuoIhOp0N0dDQ6dOiAJk2aAABUKhWsra3h5ORkNNbd3R0qlUo/5sEmtmh/0b7HjdFoNLh//z7u3LkDrVb70DFnzpwpcS3/lJubi9zcXP1jjUajf606ne6xefxbOp0OQohyP09FwCwKmSIH/5p2+H10O8zcdg5L9l7G0n2XkXDpNua+1hzPuNuX23lLi58JA2ZhwCwMmEUh5mBgyixKcw6zaWQjIyORnJyMPXv2yF1KmZkxYwamT59ebPvNmzeRk1O+1xDqdDqo1WoIIaBQmNXEu8kxi0KmzGFk2xpoWtMSn8RexllVFnot2IsxnWqjTzNXSJJUrucuCX4mDJiFAbMwYBaFmIOBKbPIysoq8VizaGSjoqKwceNGxMfHo3bt2vrtHh4eyMvLQ2ZmptFMaFpaGjw8PPRj/rm6QNFKAg+O+efqAmlpaXBwcICtrS0sLCxgYWHx0DEPHuNJtfzT5MmTERMTo3+s0Wjg7e0NV1dXODg4lCSap6bT6SBJElxdXfnLxywAmD6HCDc3dAjwwcTfjmPXuVv4esc1JKly8UXvpnCxsy738z8OPxMGzMKAWRgwi0LMwcCUWSiVyhKPlbWRFUJgzJgxWLt2LXbu3Ak/Pz+j/a1bt4aVlRXi4uLQp08fAMDZs2dx9epVBAcHAwCCg4Px2WefIT09HW5ubgCA2NhYODg4ICAgQD9m8+bNRseOjY3VH8Pa2hqtW7dGXFwcIiIiABS+YXFxcYiKiipxLf9kY2MDGxubYtsVCoVJfiEkSTLZucwdsyhk6hzcHW2xZEgglu67jC+2nMGfp9Pxwrw9mP1aC3SoV9MkNTwKPxMGzMKAWRgwi0LMwcBUWZTm+LI2spGRkVi+fDn+97//wd7eXn+tqaOjI2xtbeHo6Ihhw4YhJiYGLi4ucHBwwJgxYxAcHIx27doBALp164aAgAAMGjQIM2fOhEqlwocffojIyEh9Ezlq1CgsWLAAEydOxJtvvont27dj9erV2LRpk76WmJgYDB48GG3atEFgYCDmzJmD7OxsDB06VF/Tk2ohouIUCglvhvihnX8NjF15FBfS7+L1Hw9gRCd/vNO1Aawt+X8ORET0lMp6yYTSAPDQnyVLlujH3L9/X7z99tvC2dlZVKtWTbz88ssiNTXV6DiXL18WPXr0ELa2tqJmzZrinXfeEfn5+UZjduzYIVq0aCGsra2Fv7+/0TmKzJ8/X/j4+Ahra2sRGBgo9u/fb7S/JLU8DpffkgezKGQOOdzLLRDv/35c+E7aKHwnbRQvztstLqZnmbwOc8jCXDALA2ZhwCwKMQcDc11+SxJC8DY8JqLRaODo6Ai1Wm2Sa2SLLreo6n8OYRaFzCmHbSdVmPTbcWTey0c1awtMe6kxXm1d22RfBDOnLOTGLAyYhQGzKMQcDEyZRWn6par9rhCRLMIae2DruE4I9q+Be3laTPz1OKJWHIX6fr7cpRERUQXCRpaIZOHhqMTPw4MwsXsDWCokbDqeihfm7sahyxlyl0ZERBUEG1kiko2FQsLbz9bDr6Pbw7dGNVzPvI/Xvk/AN7HnUKDlAuRERPR4bGSJSHYtvJ2waWxH9GlVGzoBzIs7j9cW78e1jHtyl0ZERGaMjSwRmYXqNpaY1bc55vZrAXsbSyReuYMX5u7G+mM35C6NiIjMFBtZIjIrvVrUwuZxHdHa1xlZuQUYu+Io3ll9DHdzC+QujYiIzAwbWSIyO94u1bBqRDuMe74+FBLw25G/ED5vN5KuZcpdGhERmRE2skRkliwtFBjf9RmsGhmMWk62uHL7Hl5ZtA/f7rwArY7LXxMRERtZIjJzbeu4YPO4jghv5okCncDMrWfx+n8OQKXOkbs0IiKSGRtZIjJ7jrZWWNC/JWa+0gzVrC2QcOk2us+Nx7aTKrlLIyIiGbGRJaIKQZIk9G3jjY1jQtC0liMy7+Vj5E+JeH/tCdzP08pdHhERyYCNLBFVKP6u1fHb6PYY2dkfALD8wFX0XLAHp25oZK6MiIhMjY0sEVU41pYKTO7RCD8PC4KbvQ0upN9FxMK9+O+eFAjBL4IREVUVbGSJqMIKqV8TW6M7IbSRO/K0Ony88RSGLj2Em1m5cpdGREQmwEaWiCo0Fztr/PBGa3wS0QQ2lgrsPHsTPebGY+fZdLlLIyKicsZGlogqPEmSMKidLzaMCUFDD3vcupuHIUsO4eMNp5BbwC+CERFVVmxkiajSeMbdHusiO2BI+zoAgP/uTUHEwn24kJ4lb2FERFQu2MgSUaWitLLAtJca479D2sDFzhqnUzV4cf4e/HLgCoQQ0OoE9l+6jT/OZGD/pdu8SxgRUQVmKXcBRETl4bmG7tg6riPeWXMMu8/fwgdrk7Hm8DWkZuYgTf9lsBR4OioxtWcAujfxlLVeIiIqPc7IElGl5eagxLKhgfgwvBEsFEDSNfUDTWwhlToHo38+gq3JqTJVSURET4uNLBFVagqFhKEd/OBczfqh+4suLJi+4RQvMyAiqmDYyBJRpXcwJQO37uY9cr8AkKrOwcGUDNMVRURE/xobWSKq9NKzcsp0HBERmQc2skRU6bnZK0s0ztHWqpwrISKissRGlogqvUA/F3g6KiE9YdzHG07i1A2NSWoiIqJ/j40sEVV6FgoJU3sGAECxZrbosaOtJS7duoeIb/di2b7LEIJf/CIiMndsZImoSujexBOLXm8FD0fjyww8HJX47vVW2PFuF4Q2ckNegQ5T15/EW/+XiDvZj/6CGBERyY83RCCiKqN7E090DfDAgUu3cOGvm6hX2xVB/jVhoSicl/3hjTZYtu8yPt98Bn+eTkOPubsxp18LtPOvIXPlRET0MJyRJaIqxUIhoZ1/DXRr6IJ2/jX0TSwASJKEIR38sDayPfxd7aDS5GDAD/vxTew5FGh1MlZNREQP89QzsocPH8bq1atx9epV5OUZ//nt999//9eFERHJpbGXIzaOCcG09Sex+vBfmBd3HgkXb2FOv5ao5WQrd3lERPS3p5qRXblyJdq3b4/Tp09j7dq1yM/Px8mTJ7F9+3Y4OjqWdY1ERCZXzdoSM19pjrn9WqC6jSUOXb6DF+buxtZkldylERHR356qkf38888xe/ZsbNiwAdbW1pg7dy7OnDmDvn37wsfHp6xrJCKSTa8WtbB5bEc093aC+n4+Rv2ciA/XnUBOvlbu0oiIqrynamQvXryI8PBwAIC1tTWys7MhSRLGjx+PxYsXl2mBRERy86lRDb+OCsaoznUBAD/vv4peC/biXFqWzJUREVVtT9XIOjs7Iyur8F/gtWrVQnJyMgAgMzMT9+7dK7vqiIjMhJWFAu/1aIifhgWiZnUbnE3LwksL9mD5gatcc5aISCZP1ch26tQJsbGxAIBXX30V48aNw1tvvYX+/fvj+eefL9MCiYjMScf6rtgyriM6PeOKnHwd3l97ApHLj0B9L1/u0oiIqpynWrVgwYIFyMnJAQB88MEHsLKywr59+9CnTx98+OGHZVogEZG5cbW3wdIhbfHjnhTM3HYGm0+ocOyaGvP6t0BrXxe5yyMiqjKeakbWxcUFXl5ehQdQKPDee+9h/fr1mDVrFpydnUt8nPj4ePTs2RNeXl6QJAnr1q0z2n/37l1ERUWhdu3asLW1RUBAAL777jujMTk5OYiMjESNGjVQvXp19OnTB2lpaUZjrl69ivDwcFSrVg1ubm6YMGECCgoKjMbs3LkTrVq1go2NDerVq4elS5cWq3fhwoWoU6cOlEolgoKCcPDgwRK/ViKqXBQKCW918sdvo9vDt0Y1XM+8j77f78eC7eeh1fFSAyIiUyhxI6vRaIz++XE/JZWdnY3mzZtj4cKFD90fExODrVu34ueff8bp06cRHR2NqKgorF+/Xj9m/Pjx2LBhA9asWYNdu3bhxo0b6N27t36/VqtFeHg48vLysG/fPixbtgxLly7FlClT9GNSUlIQHh6OLl26ICkpCdHR0Rg+fDi2bdumH7Nq1SrExMRg6tSpOHLkCJo3b46wsDCkp6eX+PUSUeXTrLYTNo4JQUQLL2h1Al//cQ6v/+cAVOocuUsjIqr8RAkpFAqRlpYmhBBCkiShUCiK/RRtfxoAxNq1a422NW7cWHz88cdG21q1aiU++OADIYQQmZmZwsrKSqxZs0a///Tp0wKASEhIEEIIsXnzZqFQKIRKpdKPWbRokXBwcBC5ublCCCEmTpwoGjdubHSe1157TYSFhekfBwYGisjISP1jrVYrvLy8xIwZM0r8GtVqtQAg1Gp1iZ/ztLRarUhNTRVarbbcz2XumEUh5mBQXln8eviaaPTRFuE7aaNoMX2b+POU6slPkhk/FwbMwoBZFGIOBqbMojT9Uomvkd2+fTtcXAqv/dqxY0e5NNX/1L59e6xfvx5vvvkmvLy8sHPnTpw7dw6zZ88GACQmJiI/Px+hoaH65zRs2BA+Pj5ISEhAu3btkJCQgKZNm8Ld3V0/JiwsDKNHj8bJkyfRsmVLJCQkGB2jaEx0dDQAIC8vD4mJiZg8ebJ+v0KhQGhoKBISEh5Zf25uLnJzc/WPi2ardToddLryvd2lTqeDEKLcz1MRMItCzMGgvLJ4uaUXmns7YtzKJJy8ocGwZYcxJNgXk3o0gI2lRZmeq6zwc2HALAyYRSHmYGDKLEpzjhI3sp07d9b/s5+fH7y9vSFJktEYIQSuXbtW4pM/yfz58zFixAjUrl0blpaWUCgU+OGHH9CpUycAgEqlgrW1NZycnIye5+7uDpVKpR/zYBNbtL9o3+PGaDQa3L9/H3fu3IFWq33omDNnzjyy/hkzZmD69OnFtt+8eVP/ZbnyotPpoFarIYSAQvFUl0JXGsyiEHMwKM8sqgNY1Lsuvt17HSuPpmNpwhXsu5COT3r4w9dFWabnKgv8XBgwCwNmUYg5GJgyi6IlXkviqVYt8PPzQ2pqKtzc3Iy2Z2RkwM/PD1pt2dzxZv78+di/fz/Wr18PX19fxMfHIzIyEl5eXsVmUM3R5MmTERMTo3+s0Wjg7e0NV1dXODg4lOu5dTodJEmCq6srf/mYBQDm8CBTZPH5qx7o2iwdE9Ycx7mb9zF05RlM6xmAPq1qFZsEkBM/FwbMwoBZFGIOBqbMQqks+X/0P1UjK4R46L+I7969W6qTP879+/fx/vvvY+3atfq7iDVr1gxJSUn4+uuvERoaCg8PD+Tl5SEzM9NoVjYtLQ0eHh4AAA8Pj2KrCxStavDgmH+udJCWlgYHBwfY2trCwsICFhYWDx1TdIyHsbGxgY2NTbHtCoXCJL8QkiSZ7FzmjlkUYg4Gpsji+UYe2BLthPGrkrDv4m1M/O0E9ly4jc9ebgJ7pVW5nbe0+LkwYBYGzKIQczAwVRalOX6pGtmi2UVJkvDRRx+hWrVq+n1arRYHDhxAixYtSnPIR8rPz0d+fn6xF2NhYaG/dqJ169awsrJCXFwc+vTpAwA4e/Ysrl69iuDgYABAcHAwPvvsM6Snp+tnkGNjY+Hg4ICAgAD9mM2bNxudJzY2Vn8Ma2trtG7dGnFxcYiIiABQ+F8mcXFxiIqKKpPXS0SVk7uDEj8NC8J3uy7im9hzWH/sBpKuZWJe/5Zo4e0kd3lERBVaqRrZo0ePAiickT1x4gSsra31+6ytrdG8eXO8++67JT7e3bt3ceHCBf3jlJQUJCUlwcXFBT4+PujcuTMmTJgAW1tb+Pr6YteuXfi///s/fPPNNwAAR0dHDBs2DDExMXBxcYGDgwPGjBmD4OBgtGvXDgDQrVs3BAQEYNCgQZg5cyZUKhU+/PBDREZG6mdLR40ahQULFmDixIl48803sX37dqxevRqbNm3S1xYTE4PBgwejTZs2CAwMxJw5c5CdnY2hQ4eWJkIiqoIsFBIiu9RDO/8aGLviKK5m3MMri/bh3bAGGNHRHwqF+VxqQERUoTzNsghDhgwRGo3maZ5qZMeOHQJAsZ/BgwcLIYRITU0VQ4YMEV5eXkKpVIoGDRqIWbNmCZ1Opz/G/fv3xdtvvy2cnZ1FtWrVxMsvvyxSU1ONznP58mXRo0cPYWtrK2rWrCneeecdkZ+fX6yWFi1aCGtra+Hv7y+WLFlSrN758+cLHx8fYW1tLQIDA8X+/ftL9Xq5/JY8mEUh5mAgZxaZ9/LE278kCt9JG4XvpI3i9f/sF2ma+yavowg/FwbMwoBZFGIOBua6/JYkhCjVLWjy8/Nha2uLpKQkNGnSpMwb68pMo9HA0dERarXaJF/2Krqcoqpf18MsCjEHA7mzEEJg1aFrmLbhJHLydahZ3Rqz+rZA52dcTV6L3FmYE2ZhwCwKMQcDU2ZRmn6p1JVYWVnBx8enzFYmICKqaiRJQr9AH2wcE4KGHva4dTcPg/97EJ9vPo28Aq5XSURUUk/VUn/wwQd4//33kZGRUdb1EBFVGfXc7LEusgPeCPYFACyOv4RXv9uHK7ezZa6MiKhieKrltxYsWIALFy7Ay8sLvr6+sLOzM9p/5MiRMimOiKiyU1pZ4ONeTdChXk1M/PU4jv2lRvi8Pfg0ogkiWtaSuzwiIrP2VI1s0RJURERUNsIae6BpLUdEr0rCwZQMRK9Kwu7zt/Bxr8aws3mqf1UTEVV6T/Vvx6lTp5Z1HUREVZ6Xky1WvNUO87efx7y48/jtyF84evUO5vVviSa1HOUuj4jI7Dz1184yMzPxn//8B5MnT9ZfK3vkyBFcv369zIojIqpqLBQSokOfwcoRwfB0VOLSrWz0/nYfftyTglIuMkNEVOk9VSN7/PhxPPPMM/jyyy/x9ddfIzMzEwDw+++/Y/LkyWVZHxFRlRTo54It4zqiW4A78rQ6fLLxFIYtO4zbd3PlLo2IyGw8VSMbExODIUOG4Pz581AqlfrtL7zwAuLj48usOCKiqsypmjW+H9Qan/RqDGtLBbafSUePubux78ItuUsjIjILT9XIHjp0CCNHjiy2vVatWlCpVP+6KCIiKiRJEgYF18H6qA6o51Yd6Vm5GPjjAczcegb5Wq45S0RV21M1sjY2NtBoNMW2nzt3Dq6upr8zDRFRZdfQwwEbokLQP9AbQgDf7ryIvt8n4FrGPblLIyKSzVM1si+99BI+/vhj5OfnAyicMbh69SomTZqEPn36lGmBRERUyNbaAjN6N8PCAa1gr7TE0auZeGHebmw6nip3aUREsniqRnbWrFm4e/cu3NzccP/+fXTu3Bn16tWDvb09Pvvss7KukYiIHhDezBObx3ZEKx8nZOUUIHL5Ebz323Hcz+Otw4moanmqdWQdHR0RGxuLPXv24Pjx47h79y5atWqF0NDQsq6PiIgewtulGlaNDMacP8/h250XsfLQNRy+cgfz+7dEI08HucsjIjKJf3W7mJCQEISEhJRVLUREVApWFgpMCGuIDnVrInpVEi6k30WvhXvxYXgjDGrnC0mS5C6RiKhcPXUjGxcXh7i4OKSnp0OnM/7m7H//+99/XRgREZVM+3o1sWVcR0z49Ti2n0nHlP+dxJ7ztzDzlWZwqmYtd3lEROXmqa6RnT59Orp164a4uDjcunULd+7cMfohIiLTqlHdBj8OboOPXgyAlYWEP06locfc3Thw6bbcpRERlZunmpH97rvvsHTpUgwaNKis6yEioqckSRKGhfghyM8FY1YcRcqtbPT/YT/GPFcfY56rB0uLp74rORGRWXqqf6vl5eWhffv2ZV0LERGVgSa1HLFhTAj6tKoNnQDmxp3HgB8O4EbmfblLIyIqU0/VyA4fPhzLly8v61qIiKiMVLexxKy+zTHntRaws7bAwcsZ6DF3N7adNNx9UasT2H/pNv44k4H9l25DqxMyVkxEVHpPdWlBTk4OFi9ejD///BPNmjWDlZWV0f5vvvmmTIojIqJ/J6JlLbTwdsLYlUdx/C81Rv6UiEHtfNG2jjNmbDmDVHXO3yNT4OmoxNSeAejexFPWmomISuqpGtnjx4+jRYsWAIDk5OSyrIeIiMpYnZp2+HVUe3z9x1ksjr+En/ZfwU/7rxQbp1LnYPTPR7Do9VZsZomoQniqRnbHjh1lXQcREZUja0sF3n+hEdr5uWD4/x3Gw64iEAAkANM3nELXAA9YKLgOLRGZt1I1sr17937iGEmS8Ntvvz11QUREVH5srS0f2sQWEQBS1Tk4mJKB4Lo1TFYXEdHTKFUj6+joWF51EBGRCaRn5Tx5UCnGERHJqVSN7JIlS8qrDiIiMgE3e2WZjiMikhNXxyYiqkIC/Vzg6ajE465+tbKQ4OXERpaIzB8bWSKiKsRCIWFqzwAAeGQzm68VeHH+Hmw8fsN0hRERPQU2skREVUz3Jp5Y9HoreDgaz7p6OirxSa8maOHthKycAkQtP4qJvx5Ddm6BTJUSET3eUy2/RUREFVv3Jp7oGuCBA5du4cJfN1GvtiuC/GvCQiGhX6A35v55Hgt3XsDqw3/h8OU7mNe/JZrU4hd+ici8cEaWiKiKslBIaOdfA90auqCdfw39urFWFgq8G9YAK95qB09HJS7dysbL3+7FD/GXoONtbInIjLCRJSKih2rnXwNbxnVEWGN35GsFPtt8GoOXHES6hktzEZF5YCNLRESP5FTNGt+93hqfv9wUSisFdp+/he5zd2P7mTS5SyMiYiNLRESPJ0kSBgT5YENUCBp62CMjOw9vLj2MaetPIidfK3d5RFSFsZElIqISqe9uj3WRHTC0Qx0AwNJ9lxGxcC/Op2XJWxgRVVlsZImIqMSUVhaY2rMxlgxpixp21jijysKL8/fg5/1XIAS/CEZEpsVGloiISq1LQzdsie6IjvVrIrdAhw/XJWPkT4m4k50nd2lEVIXI2sjGx8ejZ8+e8PLygiRJWLduXbExp0+fxksvvQRHR0fY2dmhbdu2uHr1qn5/Tk4OIiMjUaNGDVSvXh19+vRBWprxlxCuXr2K8PBwVKtWDW5ubpgwYQIKCowX+N65cydatWoFGxsb1KtXD0uXLi1Wy8KFC1GnTh0olUoEBQXh4MGDZZIDEVFF5GavxLKhgfgwvBGsLCT8cSoNPebuxr6Lt+QujYiqCFkb2ezsbDRv3hwLFy586P6LFy8iJCQEDRs2xM6dO3H8+HF89NFHUCoNd6MZP348NmzYgDVr1mDXrl24ceMGevfurd+v1WoRHh6OvLw87Nu3D8uWLcPSpUsxZcoU/ZiUlBSEh4ejS5cuSEpKQnR0NIYPH45t27bpx6xatQoxMTGYOnUqjhw5gubNmyMsLAzp6enlkAwRUcWgUEgY3tEfa9/uAH9XO6g0ORj4nwP4atsZ5Gt1cpdHRJWdMBMAxNq1a422vfbaa+L1119/5HMyMzOFlZWVWLNmjX7b6dOnBQCRkJAghBBi8+bNQqFQCJVKpR+zaNEi4eDgIHJzc4UQQkycOFE0bty42LnDwsL0jwMDA0VkZKT+sVarFV5eXmLGjBklfo1qtVoAEGq1usTPeVparVakpqYKrVZb7ucyd8yiEHMwYBYGZZlFdm6+mLjmmPCdtFH4Ttooei3YI67cyi6DKk2DnwsDZlGIORiYMovS9Etme4tanU6HTZs2YeLEiQgLC8PRo0fh5+eHyZMnIyIiAgCQmJiI/Px8hIaG6p/XsGFD+Pj4ICEhAe3atUNCQgKaNm0Kd3d3/ZiwsDCMHj0aJ0+eRMuWLZGQkGB0jKIx0dHRAIC8vDwkJiZi8uTJ+v0KhQKhoaFISEh45GvIzc1Fbm6u/rFGo9G/Np2ufGcqdDodhBDlfp6KgFkUYg4GzMKgLLNQWiowo3cTdKxfA++vTUbStUy8MC8eH7/UGBEta5VBteWLnwsDZlGIORiYMovSnMNsG9n09HTcvXsXX3zxBT799FN8+eWX2Lp1K3r37o0dO3agc+fOUKlUsLa2hpOTk9Fz3d3doVKpAAAqlcqoiS3aX7TvcWM0Gg3u37+PO3fuQKvVPnTMmTNnHvkaZsyYgenTpxfbfvPmTeTklO+dcXQ6HdRqNYQQUCiq9nf6mEUh5mDALAzKI4s27hb4vwGNMHVrCo7duIuYNcfxx4m/MKGLD+xsLMrkHOWBnwsDZlGIORiYMousrJIv6We2jWxRN96rVy+MHz8eANCiRQvs27cP3333HTp37ixneSUyefJkxMTE6B9rNBp4e3vD1dUVDg4O5XpunU4HSZLg6urKXz5mAYA5PIhZGJRXFm5uwJrRXvh250XM234BW89k4FT6fcx5rQVaeDuV2XnKEj8XBsyiEHMwMGUWD34X6knMtpGtWbMmLC0tERAQYLS9UaNG2LNnDwDAw8MDeXl5yMzMNJqVTUtLg4eHh37MP1cXKFrV4MEx/1zpIC0tDQ4ODrC1tYWFhQUsLCweOqboGA9jY2MDGxubYtsVCoVJfiEkSTLZucwdsyjEHAyYhUF5ZWGtUCC6awOE1HfFuJVJuJpxH32/34/xXZ/BqM51YaGQyvR8ZYGfCwNmUYg5GJgqi9Ic32zfFWtra7Rt2xZnz5412n7u3Dn4+voCAFq3bg0rKyvExcXp9589exZXr15FcHAwACA4OBgnTpwwWl0gNjYWDg4O+iY5ODjY6BhFY4qOYW1tjdatWxuN0el0iIuL048hIqKHa1PHBZvHdcSLzTxRoBP4attZvP6fA1Cpy/cSKyKq/GSdkb179y4uXLigf5ySkoKkpCS4uLjAx8cHEyZMwGuvvYZOnTqhS5cu2Lp1KzZs2ICdO3cCABwdHTFs2DDExMTAxcUFDg4OGDNmDIKDg9GuXTsAQLdu3RAQEIBBgwZh5syZUKlU+PDDDxEZGamfLR01ahQWLFiAiRMn4s0338T27duxevVqbNq0SV9bTEwMBg8ejDZt2iAwMBBz5sxBdnY2hg4darrAiIgqKEdbK8zv3xKdn3HF1PUnkXDpNrrPjceXfZohrPGj/7JFRPRYZb5mQins2LFDACj2M3jwYP2YH3/8UdSrV08olUrRvHlzsW7dOqNj3L9/X7z99tvC2dlZVKtWTbz88ssiNTXVaMzly5dFjx49hK2trahZs6Z45513RH5+frFaWrRoIaytrYW/v79YsmRJsXrnz58vfHx8hLW1tQgMDBT79+8v1evl8lvyYBaFmIMBszCQI4uL6VnixXm79ct0vf/7cXEvt8Bk538Ufi4MmEUh5mBgrstvSULw5timotFo4OjoCLVabZIve6Wnp8PNza3KX9fDLAoxBwNmYSBXFnkFOsz64yy+j78EAKjvVh3z+rdEI8/y/Xfj4/BzYcAsCjEHA1NmUZp+qWq/K0REJAtrSwUmv9AIPw0LhKu9Dc6n30WvhXuxZG8KOL9CRCXFRpaIiGTTsb4rto7riOcauiGvQIfpG05h2LLDuH0398lPJqIqj40sERHJqkZ1G/w4uA2m9QyAtaUC28+ko/vc3Yg/d1Pu0ojIzLGRJSIi2UmShCEd/PC/yA6o71YdN7Ny8cZ/D+LzzaeRV8DbgxLRw7GRJSIis9HI0wHro0IwMMgHALA4/hJ6L9qLSzfvylwZEZkjNrJERGRWbK0t8NnLTfH9oNZwqmaF5OsavDh/D1YfvsYvghGRETayRERklsIae2DLuI5o5++Ce3laTPz1OKJWHIX6fr7cpRGRmWAjS0REZsvT0Ra/DG+HCWENYKGQsOl4Kl6YuxuHL2fIXRoRmQE2skREZNYsFBIiu9TDr6OC4eNSDdcz76Pv9wmY8+c5FGj5RTCiqoyNLBERVQgtfZyxaWwIeresBZ0A5vx5Hv1/2I+/7tyTuzQikgkbWSIiqjDslVb45rUWmPNaC1S3scShy3fQY+5ubDqeKndpRCQDNrJERFThRLSshc1jO6KFtxOycgoQufwIJv56DPfyCuQujYhMiI0sERFVSD41qmHNqGBEdakHSQJWH/4LL87bg+TrarlLIyITYSNLREQVlpWFAu+GNcDy4e3g4aDEpVvZePnbvfgh/hJ0Oq45S1TZsZElIqIKL7huDWwZ1xHdAtyRrxX4bPNpDF5yEOlZOXKXRkTliI0sERFVCs521vh+UGt89nITKK0U2H3+FnrM2Y3tZ9LkLo2IygkbWSIiqjQkScLAIF9siApBQw973M7Ow5tLD2Pa+pPIydfKXR4RlTE2skREVOnUd7fHusgOGNK+DgBg6b7LiFi4F+fTsuQtjIjKFBtZIiKqlJRWFpj2UmP8d0gb1LCzxhlVFnou2INfDlyBEPwiGFFlwEaWiIgqtecaumPLuI7oWL8mcvJ1+GBtMkb9nIg72Xn6MVqdwP5Lt/HHmQzsv3QbWq54QFQhWMpdABERUXlzc1Bi2dBA/LgnBTO3ncG2k2k4dm03Zr/WAur7eZi+4RRS1UUrHKTA01GJqT0D0L2Jp6x1E9HjcUaWiIiqBIVCwlud/PH76A7wq2kHlSYH/X/Yj1E/H3mgiS2kUudg9M9HsDWZt74lMmdsZImIqEppWtsRG8eE4JXWtR45pujCgukbTvEyAyIzxkaWiIiqHDsbS/Rp5f3YMQJAqjoHB1MyTFMUEZUaG1kiIqqSSnrXL94djMh8sZElIqIqyc1eWabjiMj02MgSEVGVFOjnAk9HJaTHjLGxVKBOzWomq4mISoeNLBERVUkWCglTewYAwCOb2dwCHbrP2Y11R6/zJgpEZoiNLBERVVndm3hi0eut4OFofPmAp6MSH4Y3QmMvB6jv5yN6VRJG/JTI62WJzAxviEBERFVa9yae6BrggQOXbuHCXzdRr7YrgvxrwkIhYXD7Oli08yLmbz+P2FNpOHQ5A9NfaoyXmntBkh53UQIRmQJnZImIqMqzUEho518D3Rq6oJ1/DVgoCptUKwsFxj5fH+ujQtDYywGZ9/IxbmUSRnJ2lsgssJElIiJ6gkaeDlgX2QHjQ5+BpULCH6fS0G12PP6XxGtnieTERpaIiKgErCwUGBdaODsb4Gk8O3szK1fu8oiqJDayREREpRDg5YD/RRnPznadvYuzs0QyYCNLRERUSo+anR31M2dniUyJjSwREdFTKpqdjQ6tD0uFhG0n09Bt9i6sP3aDs7NEJiBrIxsfH4+ePXvCy6twGZN169Y9cuyoUaMgSRLmzJljtD0jIwMDBw6Eg4MDnJycMGzYMNy9e9dozPHjx9GxY0colUp4e3tj5syZxY6/Zs0aNGzYEEqlEk2bNsXmzZuN9gshMGXKFHh6esLW1hahoaE4f/78U792IiKqHKwsFIgOfQb/i+qARp4OuHMvH2NXHMXon49wdpaonMnayGZnZ6N58+ZYuHDhY8etXbsW+/fvh5eXV7F9AwcOxMmTJxEbG4uNGzciPj4eI0aM0O/XaDTo1q0bfH19kZiYiK+++grTpk3D4sWL9WP27duH/v37Y9iwYTh69CgiIiIQERGB5ORk/ZiZM2di3rx5+O6773DgwAHY2dkhLCwMOTlcfoWIiIDGXo74X6RhdnbrSRW6zd6FDZydJSo/wkwAEGvXri22/a+//hK1atUSycnJwtfXV8yePVu/79SpUwKAOHTokH7bli1bhCRJ4vr160IIIb799lvh7OwscnNz9WMmTZokGjRooH/ct29fER4ebnTeoKAgMXLkSCGEEDqdTnh4eIivvvpKvz8zM1PY2NiIFStWlPg1qtVqAUCo1eoSP+dpabVakZqaKrRabbmfy9wxi0LMwYBZGDALg7LMIvl6pug+J174TtoofCdtFCP/77C4mZVTBlWaBj8XhZiDgSmzKE2/ZNZ39tLpdBg0aBAmTJiAxo0bF9ufkJAAJycntGnTRr8tNDQUCoUCBw4cwMsvv4yEhAR06tQJ1tbW+jFhYWH48ssvcefOHTg7OyMhIQExMTFGxw4LC9Nf6pCSkgKVSoXQ0FD9fkdHRwQFBSEhIQH9+vV7aP25ubnIzTX8WUmj0ehfl06nK30gpaDT6SCEKPfzVATMohBzMGAWBszCoCyzaORhj7Wjg/Htzov4dudFbD2pwoGU25j+UmOEN/Uw+7uC8XNRiDkYmDKL0pzDrBvZL7/8EpaWlhg7duxD96tUKri5uRlts7S0hIuLC1QqlX6Mn5+f0Rh3d3f9PmdnZ6hUKv22B8c8eIwHn/ewMQ8zY8YMTJ8+vdj2mzdvlvslCTqdDmq1GkIIKBRV+zt9zKIQczBgFgbMwqA8shjQzBFtPBri4z8u48Kt+xi7MglrDzthwnM+cKlmVSbnKA/8XBRiDgamzCIrK6vEY822kU1MTMTcuXNx5MgRs/8v10eZPHmy0UyvRqOBt7c3XF1d4eDgUK7n1ul0kCQJrq6u/OVjFgCYw4OYhQGzMCivLNzcgI0NfbBw50Us2nkROy5k4tiN7MLZ2WaeZXaessTPRSHmYGDKLJRKZYnHmm0ju3v3bqSnp8PHx0e/TavV4p133sGcOXNw+fJleHh4ID093eh5BQUFyMjIgIeHBwDAw8MDaWlpRmOKHj9pzIP7i7Z5enoajWnRosUjX4ONjQ1sbGyKbVcoFCb5hZAkyWTnMnfMohBzMGAWBszCoLyyUFor8E63Bghr7IF31xzDGVUWxqxMwpaTKnzcqwlqVi/+/xVy4+eiEHMwMFUWpTm+2b4rgwYNwvHjx5GUlKT/8fLywoQJE7Bt2zYAQHBwMDIzM5GYmKh/3vbt26HT6RAUFKQfEx8fj/z8fP2Y2NhYNGjQAM7OzvoxcXFxRuePjY1FcHAwAMDPzw8eHh5GYzQaDQ4cOKAfQ0RE9CRNajlifVQIxj5fuLLB5hMqdJsdj03HU+UujahCknVG9u7du7hw4YL+cUpKCpKSkuDi4gIfHx/UqFHDaLyVlRU8PDzQoEEDAECjRo3QvXt3vPXWW/juu++Qn5+PqKgo9OvXT79U14ABAzB9+nQMGzYMkyZNQnJyMubOnYvZs2frjztu3Dh07twZs2bNQnh4OFauXInDhw/rl+iSJAnR0dH49NNPUb9+ffj5+eGjjz6Cl5cXIiIiyjklIiKqTKwtFYjp+gy6BbjrZ2cjlx/BphMe+KRXE9Qww9lZInMl64zs4cOH0bJlS7Rs2RIAEBMTg5YtW2LKlCklPsYvv/yChg0b4vnnn8cLL7yAkJAQozViHR0d8ccffyAlJQWtW7fGO++8gylTphitNdu+fXssX74cixcvRvPmzfHrr79i3bp1aNKkiX7MxIkTMWbMGIwYMQJt27bF3bt3sXXr1lJdx0FERFREPzv7XD1Y/D0725Wzs0SlIgnBVZpNRaPRwNHREWq12iRf9kpPT4ebm1uVv66HWRRiDgbMwoBZGMiZRfJ1tX52FgDCm3ri416NZZud5eeiEHMwMGUWpemXqva7QkREZAaKZmfH/D07u+lEKrrNjsfmE5ydJXocNrJERERmwNqycGWDdW93QAN3e9zOzsPbvxxB5PIjuH0398kHIKqC2MgSERGZkaa1HbF+TAfD7OzxwtnZLZydJSqGjSwREZGZsbG0KDY7O/rv2dmM7Dy5yyMyG2xkiYiIzFTR7GxUF8PsbNdvdnF2luhvbGSJiIjMmI2lBd4Na4C1b7fHM+7V9bOzUZydJWIjS0REVBE0q+2EDWNCENmlLiwUEjYeT0W32buwNZmzs1R1sZElIiKqIGwsLTAhrKF+dvbW3TyM+vkIxqw4ytlZqpLYyBIREVUw/5yd3XDsxt+zsyq5SyMyKTayREREFdDDZ2cTMWbFUdzh7CxVEWxkiYiIKrCi2dm3n60LhQRsOHYDXTk7S1UEG1kiIqIKzsbSAhO7N8TatzugvpthdnYsZ2epkmMjS0REVEk093bCxrGG2dn1x26g6+x4bDvJ2VmqnNjIEhERVSLFZ2dzMfKnRIxbydlZqnzYyBIREVVCzb0Lr50d/ffs7P+SCmdn/+DsLFUibGSJiIgqKaWVBSZ1b4jf3+6Aen/Pzo7g7CxVImxkiYiIKrkW3k7YOCYEozpzdpYqFzayREREVYDSygLv9WiI30a3N5qdjV55FJn3DLOzWp3A/ku38ceZDOy/dBtanZCxaqLHs5S7ACIiIjKdlj7O2DgmBHP+PI/F8RexLukG9l68jc9fbgqtTofpG04hVZ3z9+gUeDoqMbVnALo38ZS1bqKH4YwsERFRFfPg7GxdVzvczMrFW/93GKN+PvJAE1tIpc7B6J+PYGtyqkzVEj0aG1kiIqIqqqWPMzaN7Yi3Ovk9ckzRhQXTN5ziZQZkdtjIEhERVWFKKws818D9sWMEgFR1Dg6mZJimKKISYiNLRERUxaVn5Tx5UCnGEZkKG1kiIqIqzs1eWaJx+VpeWkDmhY0sERFRFRfo5wJPRyWkJ4ybsOYYYlYl4fKtbJPURfQkbGSJiIiqOAuFhKk9AwCgWDNb9LhZbUcIAL8fvY7nv9mFib8ew7WMe6Ysk6gYNrJERESE7k08sej1VvBwNL7MwMNRie9eb4X1USFYH9UBXRq4QqsTWH34L3T5eicm/34C1zPvy1Q1VXW8IQIREREBKGxmuwZ44MClW7jw103Uq+2KIP+asFAUzss2q+2EJUMDceTqHcyOPYfd529hxcGr+C3xL/QL9Mbbz9Yr1ggTlSfOyBIREZGehUJCO/8a6NbQBe38a+ib2Ae18nHGT8OCsHpkMIL9ayBPq8P/JVxBp692YPqGk1zdgEyGjSwRERE9lUA/F6wY0Q7L3wpC2zrOyCvQYcney+g0cwc+33wat+/myl0iVXJsZImIiOhfaV+3JlaPDMZPwwLRwtsJOfk6LI6/hI4zd2Dm1jO4k50nd4lUSbGRJSIion9NkiR0rO+KtW+3x5KhbdGstiPu5Wnx7c6L6DhzB76JPQf1/Xy5y6RKho0sERERlRlJktClgRv+F9kBP7zRBo08HXA3twDz4s4j5MvtmBd3Hlk5bGipbLCRJSIiojInSRK6Brhj05gQLBrYCs+4V0dWTgG+iT2HjjN34NudF5CdWyB3mVTBsZElIiKicqNQSOjR1BNbx3XC/P4tUdfVDpn38jFz61l0nLkDi+Mv4n6eVu4yqYKStZGNj49Hz5494eXlBUmSsG7dOv2+/Px8TJo0CU2bNoWdnR28vLzwxhtv4MaNG0bHyMjIwMCBA+Hg4AAnJycMGzYMd+/eNRpz/PhxdOzYEUqlEt7e3pg5c2axWtasWYOGDRtCqVSiadOm2Lx5s9F+IQSmTJkCT09P2NraIjQ0FOfPny+7MIiIiCoxhUJCz+Ze+GN8Z8x+rTnq1KiGjOw8fL75DDrO3IH/7klBTj4bWiodWRvZ7OxsNG/eHAsXLiy27969ezhy5Ag++ugjHDlyBL///jvOnj2Ll156yWjcwIEDcfLkScTGxmLjxo2Ij4/HiBEj9Ps1Gg26desGX19fJCYm4quvvsK0adOwePFi/Zh9+/ahf//+GDZsGI4ePYqIiAhEREQgOTlZP2bmzJmYN28evvvuOxw4cAB2dnYICwtDTg7XyiMiIiopC4WEl1vWxp8xnTHzlWao7WyLW3dz8fHGU+j81Q78X8Jl5BawoaUSEmYCgFi7du1jxxw8eFAAEFeuXBFCCHHq1CkBQBw6dEg/ZsuWLUKSJHH9+nUhhBDffvutcHZ2Frm5ufoxkyZNEg0aNNA/7tu3rwgPDzc6V1BQkBg5cqQQQgidTic8PDzEV199pd+fmZkpbGxsxIoVK0r8GtVqtQAg1Gp1iZ/ztLRarUhNTRVarbbcz2XumEUh5mDALAyYhQGzMDBlFrn5WrH8wBUR/PmfwnfSRuE7aaMI/vxP8cv+KyI3X973gp8JA1NmUZp+qUJdI6tWqyFJEpycnAAACQkJcHJyQps2bfRjQkNDoVAocODAAf2YTp06wdraWj8mLCwMZ8+exZ07d/RjQkNDjc4VFhaGhIQEAEBKSgpUKpXRGEdHRwQFBenHEBERUelZWyrQP9AHOyY8i096NYa7gw1uqHPw/toTeG7WTqw+fA0FWp3cZZKZspS7gJLKycnBpEmT0L9/fzg4OAAAVCoV3NzcjMZZWlrCxcUFKpVKP8bPz89ojLu7u36fs7MzVCqVftuDYx48xoPPe9iYh8nNzUVuruGuJhqNBgCg0+mg05XvL6VOp4MQotzPUxEwi0LMwYBZGDALA2ZhIEcWVgoJA4N88EqrWlh+8BoW7bqIv+7cx8Rfj2PhjgsY+1w9vNTc66G3zC0v/EwYmDKL0pyjQjSy+fn56Nu3L4QQWLRokdzllNiMGTMwffr0Yttv3rxZ7tfW6nQ6qNVqCCGgUFSoifcyxywKMQcDZmHALAyYhYHcWYTXr4bn6zTGb8dv4qfDKly5fQ/vrDmOeX+exfB2Xnj+GWcopPJvaOXOwZyYMousrKwSjzX7Rraoib1y5Qq2b9+un40FAA8PD6SnpxuNLygoQEZGBjw8PPRj0tLSjMYUPX7SmAf3F23z9PQ0GtOiRYtH1j558mTExMToH2s0Gnh7e8PV1dXodZQHnU4HSZLg6urKXz5mAYA5PIhZGDALA2ZhYC5ZjK/lgRHPNcL/7b+CH+JTcOVOLj7akoKfEm9i7PP10L2xBxTlOENrLjmYA1NmoVQqSzzWrBvZoib2/Pnz2LFjB2rUqGG0Pzg4GJmZmUhMTETr1q0BANu3b4dOp0NQUJB+zAcffID8/HxYWVkBAGJjY9GgQQM4Ozvrx8TFxSE6Olp/7NjYWAQHBwMA/Pz84OHhgbi4OH3jqtFocODAAYwePfqR9dvY2MDGxqbYdoVCYZJfCEmSTHYuc8csCjEHA2ZhwCwMmIWBuWRhb2uNyC718UZwHSzdexk/7L6Ec+l3EbUiCY08HTA+tD66BrhDKqcZWnPJwRyYKovSHF/Wd+Xu3btISkpCUlISgMIvVSUlJeHq1avIz8/HK6+8gsOHD+OXX36BVquFSqWCSqVCXl4eAKBRo0bo3r073nrrLRw8eBB79+5FVFQU+vXrBy8vLwDAgAEDYG1tjWHDhuHkyZNYtWoV5s6dazRTOm7cOGzduhWzZs3CmTNnMG3aNBw+fBhRUVEACt+46OhofPrpp1i/fj1OnDiBN954A15eXoiIiDBpZkRERFWRvdIKY56vj92TnsPY5+ujuo0lTqdqMOKnRLy0YC92nEmHEELuMsnUynjFhFLZsWOHAFDsZ/DgwSIlJeWh+wCIHTt26I9x+/Zt0b9/f1G9enXh4OAghg4dKrKysozOc+zYMRESEiJsbGxErVq1xBdffFGsltWrV4tnnnlGWFtbi8aNG4tNmzYZ7dfpdOKjjz4S7u7uwsbGRjz//PPi7NmzpXq9XH5LHsyiEHMwYBYGzMKAWRhUhCzuZOeKmVtPi0YfbdEv29VrwR6x62y60Ol0ZXKOipCDqZjr8luSEPzPF1PRaDRwdHSEWq02yTWy6enpcHNzq/J/DmEWhZiDAbMwYBYGzMKgImVx+24uFsdfwrKEy8jJL/y2e9s6zhjf9Rm0r1vzXx27IuVQ3kyZRWn6par9rhAREVGFVqO6DSa/0AjxE7vgzQ5+sLZU4NDlOxjwwwH0W5yAgykZcpdI5YiNLBEREVV4bvZKTOkZgN0Tu2BwsC+sLRTYfykDfb9PwKAfDyDxyh25S6RywEaWiIiIKg13ByWm92qCHROexYAgH1gqJOw+fwt9Fu3DkCUHcexaptwlUhliI0tERESVTi0nW3z+clPsePdZvNbGGxYKCTvP3kSvhXsxfNlhnLyhlrtEKgNsZImIiKjS8naphi9faYa4mM7o3aoWFBLw5+k0hM/bg1E/JeKsquR3kSLzw0aWiIiIKr06Ne3wTd8WiI3pjJeae0GSgK0nVeg+Nx5Ry4/gQrpxQ6vVCey/dBt/nMnA/ku3odVxkSdzZNZ39iIiIiIqS3Vdq2Ne/5aIeq4e5v55HptOpGLj8VRsOpGKXs29MC70GZxVaTB9wymkqnP+flYKPB2VmNozAN2beD72+GRabGSJiIioynnG3R4LB7ZCVKoGs2PP4Y9TaViXdAPrj93AwyZfVeocjP75CBa93orNrBnhpQVERERUZTXydMDiN9pgQ1QIujRwfWgTCxTeWhQApm84xcsMzAgbWSIiIqrymtZ2xIhOdR87RgBIVefwJgtmhI0sEREREYD0rJwnDyrFOCp/bGSJiIiIUHh3sJL483Q67mTnlXM1VBJsZImIiIgABPq5wNNRCekJ4zYcu4GOM3fgm9hzUN/PN0lt9HBsZImIiIgAWCgkTO0ZAADFmlnp75/RneuikacD7uYWYF7ceXT8cjsW7riA7NwCU5dLYCNLREREpNe9iScWvd4KHo7Glxl4OCqx6PVWmNSjITaNCcG3A1uhvlt1aHIK8NW2s+g4cwd+iL+E+3lamSqvmriOLBEREdEDujfxRNcADxy4dAsX/rqJerVdEeRfExaKwnlahULCC009EdbYAxuO3cCcP8/h8u17+GzzaSzefQmRz9ZF/yAf2FhayPxKKj/OyBIRERH9g4VCQjv/GujW0AXt/Gvom9h/joloWQt/xnTGzFeaoZaTLW5m5WLahlPo8tVOLD9wFflanQzVVx1sZImIiIj+BUsLBfq28caOd5/FpxFN4OGgxA11Dt5fewLPzdqJXxP/QgEb2nLBRpaIiIioDFhbKvB6O1/snPAsprwYgJrVbXAt4z7eXXMM3WbH439J16HjXcHKFBtZIiIiojKktLLAmyF+iJ/4LCb3aAjnala4dCsb41Ymocfc3dianAoh2NCWBTayREREROWgmrUlRnaui92TnsM7XZ+BvdISZ9OyMOrnI3hx/h5sP5PGhvZfYiNLREREVI6q21hizPP1sWficxjzXD3YWVvg5A0N3lx6GC9/uw+7z99kQ/uU2MgSERERmYBjNSu8060Bdk96DiM7+0NppUDStUwM+vEgXlu8Hwcu3Za7xAqHjSwRERGRCbnYWWNyj0aIn9gFQzvUgbWFAgdTMvDa4v0Y9OMBHL16R+4SKww2skREREQycLNXYmrPxtg18VkMDPKBpULC7vO38PK3+zBs6SEkX1fLXaLZYyNLREREJCNPR1t89nJT7Hj3WbzaujYsFBLizqTjxfl7MOqnRJxVZcldotliI0tERERkBrxdquGrV5sjdnwn9GrhBUkCtp5UofvceIxdcRSXbt6Vu0Szw0aWiIiIyIz4u1bH3H4tsS26E15o6gEhgPXHbiD0m114d80xXMu4J3eJZoONLBEREZEZesbdHt8ObI1NY0MQ2sgNOgH8mvgXuny9E5N/P4EbmfflLlF2bGSJiIiIzFhjL0f8Z3BbrIvsgI71a6JAJ7Di4FU8+9VOTFt/EumaHLlLlA0bWSIiIqIKoIW3E34aFoQ1o4IR5OeCPK0OS/ddRqevduDzzadx+26u3CWaHBtZIiIiogqkbR0XrBzRDr8MD0IrHyfk5OuwOP4SOs7cga+3nYX6Xr7cJZoMG1kiIiKiCkaSJHSoVxO/jW6PJUPbomktR9zL02LBjgsImbkd8+LOIyun8je0bGSJiIiIKihJktClgRvWR3XA94Nao6GHPbJyCvBN7Dl0nLkDi3ZexL28ArnLLDdsZImIiIgqOEmSENbYA5vHdsT8/i1R19UOmffy8eXWM+g0cwd+3JOCnHyt3GWWOTayRERERJWEQiGhZ3Mv/DG+M77p2xw+LtVw624ePtl4Cp2/2oGf9l9BXoFO7jLLjKyNbHx8PHr27AkvLy9IkoR169YZ7RdCYMqUKfD09IStrS1CQ0Nx/vx5ozEZGRkYOHAgHBwc4OTkhGHDhuHuXeM7Xxw/fhwdO3aEUqmEt7c3Zs6cWayWNWvWoGHDhlAqlWjatCk2b95c6lqIiIiIzIGFQkLvVrUR905nfNG7KWo52SJNk4uP1iWjy9c7serQVeRrK35DK2sjm52djebNm2PhwoUP3T9z5kzMmzcP3333HQ4cOAA7OzuEhYUhJ8ewXtrAgQNx8uRJxMbGYuPGjYiPj8eIESP0+zUaDbp16wZfX18kJibiq6++wrRp07B48WL9mH379qF///4YNmwYjh49ioiICERERCA5OblUtRARERGZEysLBfoF+mD7u53xca/GcLO3wfXM+5j02wl0/WYX1h79C1qdkLvMpyfMBACxdu1a/WOdTic8PDzEV199pd+WmZkpbGxsxIoVK4QQQpw6dUoAEIcOHdKP2bJli5AkSVy/fl0IIcS3334rnJ2dRW5urn7MpEmTRIMGDfSP+/btK8LDw43qCQoKEiNHjixxLSWhVqsFAKFWq0v8nKel1WpFamqq0Gq15X4uc8csCjEHA2ZhwCwMmIUBsyhUGXO4n1cgfoi/KFp9/IfwnbRR+E7aKJ6ftVNsPHZDaLW6Rz7PlFmUpl8y22tkU1JSoFKpEBoaqt/m6OiIoKAgJCQkAAASEhLg5OSENm3a6MeEhoZCoVDgwIED+jGdOnWCtbW1fkxYWBjOnj2LO3fu6Mc8eJ6iMUXnKUktREREROZOaWWB4R39ET+xCyZ2bwBHWytcSL+LyOVH8MK83fjjpApCGM/QanUC+y/dxh9nMrD/0m2zmsG1lLuAR1GpVAAAd3d3o+3u7u76fSqVCm5ubkb7LS0t4eLiYjTGz8+v2DGK9jk7O0OlUj3xPE+q5WFyc3ORm2u4y4ZGowEA6HQ66HTle12KTqeDEKLcz1MRMItCzMGAWRgwCwNmYcAsClXmHGytFBjVyR8DAr2xZO9l/LjnMs6osjDip0Q0reWImK710al+TWw7mYaPN56GSn8b3BR4OCgx5cVG6N7Eo1xqK03eZtvIVgYzZszA9OnTi22/efNmuV9bq9PpoFarIYSAQmG2E+8mwSwKMQcDZmHALAyYhQGzKFRVcujf1BEv1G+M5YlpWJ2UjhPX1Ri69DB8nW1w5U7x296qNDl4e/lRzHjRH13qOZd5PVlZWSUea7aNrIdHYZeflpYGT09P/fa0tDS0aNFCPyY9Pd3oeQUFBcjIyNA/38PDA2lpaUZjih4/acyD+59Uy8NMnjwZMTEx+scajQbe3t5wdXWFg4PD4wP4l3Q6HSRJgqura6X+5SsJZlGIORgwCwNmYcAsDJhFoaqUgxuAqT5eiOyai8XxKfi/hMsPbWKLSADm7b6BV9o9AwuFVKa1KJXKEo8120bWz88PHh4eiIuL0zeLGo0GBw4cwOjRowEAwcHByMzMRGJiIlq3bg0A2L59O3Q6HYKCgvRjPvjgA+Tn58PKygoAEBsbiwYNGsDZ2Vk/Ji4uDtHR0frzx8bGIjg4uMS1PIyNjQ1sbGyKbVcoFCb5hZAkyWTnMnfMohBzMGAWBszCgFkYMItCVS0HNwdbfPhiAFr5OuPtX448cpwAkKrOweErmQiuW6NMayhN1rK+K3fv3kVSUhKSkpIAFH6pKikpCVevXoUkSYiOjsann36K9evX48SJE3jjjTfg5eWFiIgIAECjRo3QvXt3vPXWWzh48CD27t2LqKgo9OvXD15eXgCAAQMGwNraGsOGDcPJkyexatUqzJ0712imdNy4cdi6dStmzZqFM2fOYNq0aTh8+DCioqIAoES1EBEREVUWJV1jNj1L3mVIZZ2RPXz4MLp06aJ/XNRcDh48GEuXLsXEiRORnZ2NESNGIDMzEyEhIdi6davRlPMvv/yCqKgoPP/881AoFOjTpw/mzZun3+/o6Ig//vgDkZGRaN26NWrWrIkpU6YYrTXbvn17LF++HB9++CHef/991K9fH+vWrUOTJk30Y0pSCxEREVFl4GZfsv6mpOPKiyT+ucYClRuNRgNHR0eo1WqTXCObnp4ONze3KvPnkEdhFoWYgwGzMGAWBszCgFkUqso5aHUCIV9uh0qdg4c1ihIAD0cl9kx6rsyvkS1Nv1S13hUiIiIieiILhYSpPQMAFDatDyp6PLVnQJk3saXFRpaIiIiIiunexBOLXm8FD0fjywc8HJVY9HordG/i+Yhnmo7ZrlpARERERPLq3sQTXQM8cODSLVz46ybq1XZFkH9N2Wdii7CRJSIiIqJHslBIaOdfA/7VtXBzqwGFmTSxAC8tICIiIqIKio0sEREREVVIbGSJiIiIqEJiI0tEREREFRIbWSIiIiKqkNjIEhEREVGFxEaWiIiIiCokNrJEREREVCGxkSUiIiKiComNLBERERFVSGxkiYiIiKhCspS7gKpECAEA0Gg05X4unU6HrKwsKJVKKBRV+79XmEUh5mDALAyYhQGzMGAWhZiDgSmzKOqTivqmx2Eja0JZWVkAAG9vb5krISIiIjJvWVlZcHR0fOwYSZSk3aUyodPpcOPGDdjb20OSpHI9l0ajgbe3N65duwYHB4dyPZe5YxaFmIMBszBgFgbMwoBZFGIOBqbMQgiBrKwseHl5PXH2lzOyJqRQKFC7dm2TntPBwaHK//IVYRaFmIMBszBgFgbMwoBZFGIOBqbK4kkzsUWq9gUfRERERFRhsZElIiIiogqJjWwlZWNjg6lTp8LGxkbuUmTHLAoxBwNmYcAsDJiFAbMoxBwMzDULftmLiIiIiCokzsgSERERUYXERpaIiIiIKiQ2skRERERUIbGRrURmzJiBtm3bwt7eHm5uboiIiMDZs2flLksWixYtQrNmzfTr3QUHB2PLli1yl2UWvvjiC0iShOjoaLlLMblp06ZBkiSjn4YNG8pdlmyuX7+O119/HTVq1ICtrS2aNm2Kw4cPy12WydWpU6fY50KSJERGRspdmklptVp89NFH8PPzg62tLerWrYtPPvmkRLcJrYyysrIQHR0NX19f2Nraon379jh06JDcZZW7+Ph49OzZE15eXpAkCevWrTPaL4TAlClT4OnpCVtbW4SGhuL8+fPyFAs2spXKrl27EBkZif379yM2Nhb5+fno1q0bsrOz5S7N5GrXro0vvvgCiYmJOHz4MJ577jn06tULJ0+elLs0WR06dAjff/89mjVrJncpsmncuDFSU1P1P3v27JG7JFncuXMHHTp0gJWVFbZs2YJTp05h1qxZcHZ2lrs0kzt06JDRZyI2NhYA8Oqrr8pcmWl9+eWXWLRoERYsWIDTp0/jyy+/xMyZMzF//ny5S5PF8OHDERsbi59++gknTpxAt27dEBoaiuvXr8tdWrnKzs5G8+bNsXDhwofunzlzJubNm4fvvvsOBw4cgJ2dHcLCwpCTk2PiSv8mqNJKT08XAMSuXbvkLsUsODs7i//85z9ylyGbrKwsUb9+fREbGys6d+4sxo0bJ3dJJjd16lTRvHlzucswC5MmTRIhISFyl2GWxo0bJ+rWrSt0Op3cpZhUeHi4ePPNN4229e7dWwwcOFCmiuRz7949YWFhITZu3Gi0vVWrVuKDDz6QqSrTAyDWrl2rf6zT6YSHh4f46quv9NsyMzOFjY2NWLFihQwVCsEZ2UpMrVYDAFxcXGSuRF5arRYrV65EdnY2goOD5S5HNpGRkQgPD0doaKjcpcjq/Pnz8PLygr+/PwYOHIirV6/KXZIs1q9fjzZt2uDVV1+Fm5sbWrZsiR9++EHusmSXl5eHn3/+GW+++SYkSZK7HJNq37494uLicO7cOQDAsWPHsGfPHvTo0UPmykyvoKAAWq0WSqXSaLutrW2V/SsOAKSkpEClUhn9/4ijoyOCgoKQkJAgS02WspyVyp1Op0N0dDQ6dOiAJk2ayF2OLE6cOIHg4GDk5OSgevXqWLt2LQICAuQuSxYrV67EkSNHqsT1XY8TFBSEpUuXokGDBkhNTcX06dPRsWNHJCcnw97eXu7yTOrSpUtYtGgRYmJi8P777+PQoUMYO3YsrK2tMXjwYLnLk826deuQmZmJIUOGyF2Kyb333nvQaDRo2LAhLCwsoNVq8dlnn2HgwIFyl2Zy9vb2CA4OxieffIJGjRrB3d0dK1asQEJCAurVqyd3ebJRqVQAAHd3d6Pt7u7u+n2mxka2koqMjERycnKV/i/HBg0aICkpCWq1Gr/++isGDx6MXbt2Vblm9tq1axg3bhxiY2OLzS5UNQ/OLDVr1gxBQUHw9fXF6tWrMWzYMBkrMz2dToc2bdrg888/BwC0bNkSycnJ+O6776p0I/vjjz+iR48e8PLykrsUk1u9ejV++eUXLF++HI0bN0ZSUhKio6Ph5eVVJT8TP/30E958803UqlULFhYWaNWqFfr374/ExES5S6MH8NKCSigqKgobN27Ejh07ULt2bbnLkY21tTXq1auH1q1bY8aMGWjevDnmzp0rd1kml5iYiPT0dLRq1QqWlpawtLTErl27MG/ePFhaWkKr1cpdomycnJzwzDPP4MKFC3KXYnKenp7F/qOuUaNGVfZSCwC4cuUK/vzzTwwfPlzuUmQxYcIEvPfee+jXrx+aNm2KQYMGYfz48ZgxY4bcpcmibt262LVrF+7evYtr167h4MGDyM/Ph7+/v9ylycbDwwMAkJaWZrQ9LS1Nv8/U2MhWIkIIREVFYe3atdi+fTv8/PzkLsms6HQ65Obmyl2GyT3//PM4ceIEkpKS9D9t2rTBwIEDkZSUBAsLC7lLlM3du3dx8eJFeHp6yl2KyXXo0KHY8nznzp2Dr6+vTBXJb8mSJXBzc0N4eLjcpcji3r17UCiM2wILCwvodDqZKjIPdnZ28PT0xJ07d7Bt2zb06tVL7pJk4+fnBw8PD8T9f3v3HxN1/ccB/Hme3KEneIAMU3aQ00NBQtD8AakYjovVFbMpcy75UakLSvSguBqeUeqBZVKjGmVAaydsLUvnD8gf5w9CRSaSCEwJKYUyfyJqpMf7+4frvp3cpZh6HT4f221+3r8+r89nE5/78L6PO3ZY2zo6OnDgwAGnfQeFWwv6kNTUVJhMJnz33Xfw8PCw7lcZPHgwBgwY4OTqHiy9Xo+4uDioVCpcvnwZJpMJZrMZ5eXlzi7tgfPw8OixT1qhUMDHx+eh2z+dkZEBrVaLgIAAtLW1wWAwQCqVYu7cuc4u7YFbsmQJIiMjsXLlSsyZMwcHDx5EYWEhCgsLnV2aU3R3d6OoqAiJiYno3//h/KdRq9VixYoVUKlUCAkJweHDh7FmzRqkpKQ4uzSnKC8vhxACQUFBOHHiBDIzMzF69GgkJyc7u7T7qrOz0+a3VC0tLaitrYW3tzdUKhXS09Px7rvvYtSoUXj00UeRnZ2NYcOGIT4+3jkFO+VdCXRfALD7KSoqcnZpD1xKSooICAgQMplM+Pr6ipiYGFFRUeHssv4zHtbXbyUkJIhHHnlEyGQyMXz4cJGQkCBOnDjh7LKcZtOmTWLs2LFCLpeL0aNHi8LCQmeX5DTl5eUCgGhqanJ2KU7T0dEhFi9eLFQqlXB3dxcjRowQb731lujq6nJ2aU5RVlYmRowYIWQymRg6dKhITU0VFy9edHZZ992uXbvsZonExEQhxM1XcGVnZws/Pz8hl8tFTEyMU//eSIR4SP/LDiIiIiJyadwjS0REREQuiUGWiIiIiFwSgywRERERuSQGWSIiIiJySQyyREREROSSGGSJiIiIyCUxyBIRERGRS2KQJSIiIiKXxCBLRERERC6JQZaI6B46efIkJBIJamtrnV2KVWNjIyZPngx3d3eMGzeu1/P/i9f0b61btw6xsbG9mpOVlYVXX331PlVERHeDQZaI+pSkpCRIJBIYjUab9m+//RYSicRJVTmXwWCAQqFAU1MTduzY4exyUFxcDKVS6bTz//HHH8jOzobBYLC2LV++vEfI37t3L5RKJdLT0yGEQEZGBkpKSvDTTz894IqJyBEGWSLqc9zd3ZGbm4sLFy44u5R75s8//7zruc3NzXjiiScQEBAAHx+fe1iVc1ksFnR3d/d63tdffw1PT09ERUU5HLN582ZoNBosXboUa9euhUQiwZAhQ6DRaPDJJ5/8m7KJ6B5ikCWiPmfmzJkYOnQoVq1a5XCMvSdwa9euRWBgoPU4KSkJ8fHxWLlyJfz8/KBUKpGTk4MbN24gMzMT3t7e8Pf3R1FRUY/1GxsbERkZCXd3d4wdOxa7d++26T969Cji4uIwaNAg+Pn54YUXXsDZs2et/dHR0UhLS0N6ero1QNnT3d2NnJwc+Pv7Qy6XY9y4cdi2bZu1XyKRoKamBjk5OZBIJFi+fLnDdfLy8jBy5EjI5XKoVCqsWLHC7lh7T1RvfeJ95MgRzJgxAx4eHvD09MT48eNx6NAhmM1mJCcn49KlS5BIJDY1dXV1ISMjA8OHD4dCocCkSZNgNpt7nHfjxo0IDg6GXC7Hzz//DLPZjIkTJ0KhUECpVCIqKgqtra12aweA0tJSaLVah/0mkwmzZs1CXl4eli1bZtOn1WpRWlrqcC4RPVgMskTU50ilUqxcuRIfffQRTp069a/W2rlzJ9ra2rBnzx6sWbMGBoMBzzzzDLy8vHDgwAEsWrQICxcu7HGezMxM6HQ6HD58GFOmTIFWq8W5c+cAABcvXsSTTz6J8PBwHDp0CNu2bcNvv/2GOXPm2KxRUlICmUyGyspKfPrpp3bry8/Px/vvv4/33nsPdXV10Gg0ePbZZ3H8+HEAQHt7O0JCQqDT6dDe3o6MjAy76+j1ehiNRmRnZ+PYsWMwmUzw8/O76/s2b948+Pv7o7q6GjU1NcjKyoKbmxsiIyOxdu1aeHp6or293aamtLQ0VFVVobS0FHV1dZg9ezaeeuop67UAwNWrV5Gbm4vPP/8c9fX18Pb2Rnx8PKZPn466ujpUVVVhwYIF/7iNZN++fZgwYYLdvoKCAiQnJ+OLL75AWlpaj/6JEyfi1KlTOHny5F3fGyK6hwQRUR+SmJgonnvuOSGEEJMnTxYpKSlCCCE2bNgg/v4jz2AwiLCwMJu5H3zwgQgICLBZKyAgQFgsFmtbUFCQmDp1qvX4xo0bQqFQiPXr1wshhGhpaREAhNFotI65fv268Pf3F7m5uUIIId555x0RGxtrc+5ffvlFABBNTU1CCCGmT58uwsPDb3u9w4YNEytWrLBpe/zxx8Urr7xiPQ4LCxMGg8HhGh0dHUIul4vPPvvMbv9f13T48GEhhBBFRUVi8ODBNmNuvb8eHh6iuLjY7nr25re2tgqpVCpOnz5t0x4TEyP0er11HgBRW1tr7T937pwAIMxms8Pr+7sLFy4IAGLPnj027QaDQchkMgFArFu3zuH8S5cu9ep8RHR/8YksEfVZubm5KCkpQUNDw12vERISgn79/v+j0s/PD6GhodZjqVQKHx8fnDlzxmbelClTrH/u378/JkyYYK3jyJEj2LVrFwYNGmT9jB49GsDN/ax/GT9+/D/W1tHRgba2th57PaOionp1zQ0NDejq6kJMTMwdz7mdpUuX4qWXXsLMmTNhNBptrsueH3/8ERaLBWq12ua+7N6922auTCbDY489Zj329vZGUlISNBoNtFot8vPz0d7e7vA8165dA3BzH/Wt/P39ERERgdWrVztcY8CAAQBuPhkmIudjkCWiPmvatGnQaDTQ6/U9+vr16wchhE3b9evXe4xzc3OzOZZIJHbbevOlo87OTmi1WtTW1tp8jh8/jmnTplnHKRSKO17z3/grnN2pO7l3y5cvR319PZ5++mns3LkTwcHB2LBhg8M1Ozs7IZVKUVNTY3NPGhoakJ+fb1PrrdsGioqKUFVVhcjISJSVlUGtVmP//v12z+Pj4wOJRGL3i4AeHh7Yvn07FAoFZsyYYTfMnj9/HgDg6+vr8FqI6MFhkCWiPs1oNGLTpk2oqqqyaff19cWvv/5qE8ju5XtS/x6kbty4gZqaGowZMwYAEBERgfr6egQGBmLkyJE2n96EV09PTwwbNgyVlZU27ZWVlQgODr7jdUaNGoUBAwbc8au5fH19cfnyZVy5csXaZu/eqdVqLFmyBBUVFZg1a5b1S3EymQwWi8VmbHh4OCwWC86cOdPjngwdOvS2NYWHh0Ov1+OHH37A2LFjYTKZ7I6TyWQIDg7GsWPH7PZ7eXlh+/bt8PT0RHR0NNra2mz6jx49Cjc3N4SEhNy2JiK6/xhkiahPCw0Nxbx58/Dhhx/atEdHR+P3339HXl4empubUVBQgK1bt96z8xYUFGDDhg1obGxEamoqLly4gJSUFABAamoqzp8/j7lz56K6uhrNzc0oLy9HcnJyj4B3O5mZmcjNzUVZWRmampqQlZWF2tpaLF68+I7XcHd3xxtvvIHXX38dX375JZqbm7F//36sW7fO7vhJkyZh4MCBePPNN9Hc3AyTyYTi4mJr/7Vr15CWlgaz2YzW1lZUVlaiurraGuQDAwPR2dmJHTt24OzZs7h69SrUajXmzZuH+fPn45tvvkFLSwsOHjyIVatWYfPmzQ5rb2lpgV6vR1VVFVpbW1FRUYHjx49bz2WPRqPBvn37HPYrlUp8//338PLy6hFm9+7di6lTp/b6KTYR3R8MskTU5+Xk5PT41f+YMWPw8ccfo6CgAGFhYTh48KDDb/TfDaPRCKPRiLCwMOzbtw8bN27EkCFDAMD6FNVisSA2NhahoaFIT0+HUqm02Y97J1577TUsXboUOp0OoaGh2LZtGzZu3IhRo0b1ap3s7GzodDosW7YMY8aMQUJCQo99v3/x9vbGV199hS1btiA0NBTr16+3ea2XVCrFuXPnMH/+fKjVasyZMwdxcXF4++23AQCRkZFYtGgREhIS4Ovri7y8PAA3twjMnz8fOp0OQUFBiI+PR3V1NVQqlcO6Bw4ciMbGRjz//PNQq9VYsGABUlNTsXDhQodzXnzxRWzZsgWXLl1yOGbw4MGoqKjAkCFDMH36dJw+fRrAzVd3vfzyyw7nEdGDJRG3bnQiIiLq42bPno2IiAi7+6cd2bp1K3Q6Herq6tC/f//7WB0R3Sk+kSUioofO6tWrMWjQoF7NuXLlCoqKihhiif5D+ESWiIiIiFwSn8gSERERkUtikCUiIiIil8QgS0REREQuiUGWiIiIiFwSgywRERERuSQGWSIiIiJySQyyREREROSSGGSJiIiIyCUxyBIRERGRS/ofslU+jpwqkFAAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# 4.5 Elbow Method for Candidate k Values\n",
        "\n",
        "inertia = []\n",
        "k_range = range(2, 11)\n",
        "\n",
        "for k in k_range:\n",
        "    kmeans_candidate = KMeans(\n",
        "        n_clusters=k,\n",
        "        random_state=RANDOM_STATE,\n",
        "        n_init=10\n",
        "    )\n",
        "\n",
        "    kmeans_candidate.fit(X_train_cluster_matrix)\n",
        "    inertia.append(kmeans_candidate.inertia_)\n",
        "\n",
        "elbow_df = pd.DataFrame({\n",
        "    \"k\": list(k_range),\n",
        "    \"inertia\": inertia\n",
        "})\n",
        "\n",
        "display(elbow_df)\n",
        "\n",
        "plt.figure(figsize=(7, 5))\n",
        "plt.plot(elbow_df[\"k\"], elbow_df[\"inertia\"], marker=\"o\")\n",
        "plt.xlabel(\"Number of clusters (K)\")\n",
        "plt.ylabel(\"Inertia\")\n",
        "plt.title(\"Elbow Method for K-Means\")\n",
        "plt.xticks(list(k_range))\n",
        "plt.grid(alpha=0.3)\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Elbow Method Interpretation\n",
        "\n",
        "The elbow method compares different values of `K` using inertia, which measures how tightly observations fit within their assigned clusters. Lower inertia is better, but inertia naturally decreases as `K` increases because more clusters allow the model to fit the data more closely.\n",
        "\n",
        "In this plot, inertia decreases steadily from approximately **255,430.80 at K=2** to **98,508.03 at K=10**. There is no single sharp elbow point where the improvement clearly levels off. This means the elbow method does not provide a definitive choice for the number of clusters.\n",
        "\n",
        "Because the elbow method is inconclusive here, the final cluster decision should not be based on inertia alone. Instead, it should be combined with silhouette score, cluster-size stability, and interpretability of the resulting clusters."
      ],
      "metadata": {
        "id": "kaRVBt0ngzFR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 4.6 K-Means Silhouette Analysis on Full Training Clustering Matrix\n",
        "\n",
        "k_values = range(2, 11)\n",
        "silhouette_results = []\n",
        "\n",
        "for k in k_values:\n",
        "    kmeans_candidate = KMeans(\n",
        "        n_clusters=k,\n",
        "        random_state=RANDOM_STATE,\n",
        "        n_init=10\n",
        "    )\n",
        "\n",
        "    candidate_labels = kmeans_candidate.fit_predict(X_train_cluster_matrix)\n",
        "\n",
        "    score = silhouette_score(\n",
        "        X_train_cluster_matrix,\n",
        "        candidate_labels\n",
        "    )\n",
        "\n",
        "    silhouette_results.append({\n",
        "        \"k\": k,\n",
        "        \"silhouette_score\": score\n",
        "    })\n",
        "\n",
        "kmeans_silhouette_df = pd.DataFrame(silhouette_results)\n",
        "\n",
        "display(kmeans_silhouette_df.round(3))\n",
        "\n",
        "plt.figure(figsize=(8, 5))\n",
        "plt.plot(\n",
        "    kmeans_silhouette_df[\"k\"],\n",
        "    kmeans_silhouette_df[\"silhouette_score\"],\n",
        "    marker=\"o\"\n",
        ")\n",
        "plt.title(\"K-Means Silhouette Score by Number of Clusters\")\n",
        "plt.xlabel(\"Number of Clusters (K)\")\n",
        "plt.ylabel(\"Silhouette Score\")\n",
        "plt.xticks(list(k_values))\n",
        "plt.grid(alpha=0.3)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 802
        },
        "id": "kwwIQwJed1Z0",
        "outputId": "3167ef2c-528e-4c30-ba6a-631d589bba87"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "    k  silhouette_score\n",
              "0   2             0.198\n",
              "1   3             0.221\n",
              "2   4             0.312\n",
              "3   5             0.250\n",
              "4   6             0.290\n",
              "5   7             0.286\n",
              "6   8             0.315\n",
              "7   9             0.314\n",
              "8  10             0.350"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f9ac9587-d50e-46e5-ad8f-73e644034cc1\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>k</th>\n",
              "      <th>silhouette_score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2</td>\n",
              "      <td>0.198</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3</td>\n",
              "      <td>0.221</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4</td>\n",
              "      <td>0.312</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5</td>\n",
              "      <td>0.250</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>6</td>\n",
              "      <td>0.290</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>7</td>\n",
              "      <td>0.286</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>8</td>\n",
              "      <td>0.315</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>9</td>\n",
              "      <td>0.314</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>10</td>\n",
              "      <td>0.350</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f9ac9587-d50e-46e5-ad8f-73e644034cc1')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-f9ac9587-d50e-46e5-ad8f-73e644034cc1 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-f9ac9587-d50e-46e5-ad8f-73e644034cc1');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"plt\",\n  \"rows\": 9,\n  \"fields\": [\n    {\n      \"column\": \"k\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 2,\n        \"max\": 10,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          9,\n          3,\n          7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"silhouette_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.04939326314837322,\n        \"min\": 0.198,\n        \"max\": 0.35,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          0.314,\n          0.221,\n          0.286\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Silhouette Score Interpretation\n",
        "\n",
        "The K-Means silhouette analysis shows that the highest score occurs at `K=10`, with a silhouette score of approximately 0.350. However, silhouette score alone should not determine the final cluster choice, because higher values of `K` can create overly small or unstable clusters.\n",
        "\n",
        "The results also show that `K=4`, `K=8`, and `K=9` perform relatively well, while `K=6` provides a moderate score. Therefore, the final cluster choice should be made by combining the silhouette score with cluster-size stability and interpretability.\n",
        "\n",
        "In this project, the goal of clustering is not to maximize the score mechanically, but to create useful job-posting segments that can be interpreted and used as engineered features in the supervised models."
      ],
      "metadata": {
        "id": "2EjKXggRiAvJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 4.7 Compare Cluster Sizes for Candidate k Values\n",
        "\n",
        "candidate_k_values = [2,3,4,5, 6, 7, 8,9,10]\n",
        "\n",
        "cluster_size_results = []\n",
        "\n",
        "for k in candidate_k_values:\n",
        "    kmeans_candidate = KMeans(\n",
        "        n_clusters=k,\n",
        "        random_state=RANDOM_STATE,\n",
        "        n_init=10\n",
        "    )\n",
        "\n",
        "    candidate_clusters = kmeans_candidate.fit_predict(X_train_cluster_matrix)\n",
        "\n",
        "    cluster_counts = pd.Series(candidate_clusters).value_counts().sort_index()\n",
        "\n",
        "    smallest_cluster_size = cluster_counts.min()\n",
        "    largest_cluster_size = cluster_counts.max()\n",
        "    smallest_cluster_pct = smallest_cluster_size / len(candidate_clusters) * 100\n",
        "    largest_cluster_pct = largest_cluster_size / len(candidate_clusters) * 100\n",
        "\n",
        "    print(f\"\\n===== K = {k} =====\")\n",
        "    print(cluster_counts)\n",
        "    print(\"Smallest cluster size:\", smallest_cluster_size)\n",
        "    print(\"Largest cluster size:\", largest_cluster_size)\n",
        "    print(\"Smallest cluster percentage:\", round(smallest_cluster_pct, 3), \"%\")\n",
        "    print(\"Largest cluster percentage:\", round(largest_cluster_pct, 3), \"%\")\n",
        "\n",
        "    cluster_size_results.append({\n",
        "        \"k\": k,\n",
        "        \"smallest_cluster_size\": smallest_cluster_size,\n",
        "        \"largest_cluster_size\": largest_cluster_size,\n",
        "        \"smallest_cluster_pct\": round(smallest_cluster_pct, 3),\n",
        "        \"largest_cluster_pct\": round(largest_cluster_pct, 3),\n",
        "        \"has_one_observation_cluster\": smallest_cluster_size == 1\n",
        "    })\n",
        "\n",
        "cluster_size_summary = pd.DataFrame(cluster_size_results)\n",
        "\n",
        "cluster_decision_df = cluster_size_summary.merge(\n",
        "    kmeans_silhouette_df,\n",
        "    on=\"k\",\n",
        "    how=\"left\"\n",
        ")\n",
        "\n",
        "cluster_decision_df = cluster_decision_df[\n",
        "    [\n",
        "        \"k\",\n",
        "        \"silhouette_score\",\n",
        "        \"smallest_cluster_size\",\n",
        "        \"largest_cluster_size\",\n",
        "        \"smallest_cluster_pct\",\n",
        "        \"largest_cluster_pct\",\n",
        "        \"has_one_observation_cluster\"\n",
        "    ]\n",
        "]\n",
        "\n",
        "display(cluster_decision_df.round(3))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "ykcUn1z83moC",
        "outputId": "2ab59958-e358-4a5c-ced5-850bbf95180f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "===== K = 2 =====\n",
            "0     6830\n",
            "1    16827\n",
            "Name: count, dtype: int64\n",
            "Smallest cluster size: 6830\n",
            "Largest cluster size: 16827\n",
            "Smallest cluster percentage: 28.871 %\n",
            "Largest cluster percentage: 71.129 %\n",
            "\n",
            "===== K = 3 =====\n",
            "0     5116\n",
            "1     2100\n",
            "2    16441\n",
            "Name: count, dtype: int64\n",
            "Smallest cluster size: 2100\n",
            "Largest cluster size: 16441\n",
            "Smallest cluster percentage: 8.877 %\n",
            "Largest cluster percentage: 69.497 %\n",
            "\n",
            "===== K = 4 =====\n",
            "0     2599\n",
            "1    17125\n",
            "2     1842\n",
            "3     2091\n",
            "Name: count, dtype: int64\n",
            "Smallest cluster size: 1842\n",
            "Largest cluster size: 17125\n",
            "Smallest cluster percentage: 7.786 %\n",
            "Largest cluster percentage: 72.389 %\n",
            "\n",
            "===== K = 5 =====\n",
            "0    15683\n",
            "1     1983\n",
            "2      526\n",
            "3     4834\n",
            "4      631\n",
            "Name: count, dtype: int64\n",
            "Smallest cluster size: 526\n",
            "Largest cluster size: 15683\n",
            "Smallest cluster percentage: 2.223 %\n",
            "Largest cluster percentage: 66.293 %\n",
            "\n",
            "===== K = 6 =====\n",
            "0     4571\n",
            "1    13055\n",
            "2     1940\n",
            "3     1451\n",
            "4     2057\n",
            "5      583\n",
            "Name: count, dtype: int64\n",
            "Smallest cluster size: 583\n",
            "Largest cluster size: 13055\n",
            "Smallest cluster percentage: 2.464 %\n",
            "Largest cluster percentage: 55.185 %\n",
            "\n",
            "===== K = 7 =====\n",
            "0      583\n",
            "1    13701\n",
            "2     1776\n",
            "3     1949\n",
            "4     4196\n",
            "5        1\n",
            "6     1451\n",
            "Name: count, dtype: int64\n",
            "Smallest cluster size: 1\n",
            "Largest cluster size: 13701\n",
            "Smallest cluster percentage: 0.004 %\n",
            "Largest cluster percentage: 57.915 %\n",
            "\n",
            "===== K = 8 =====\n",
            "0     4173\n",
            "1     2159\n",
            "2    12463\n",
            "3     1958\n",
            "4      573\n",
            "5     1747\n",
            "6      583\n",
            "7        1\n",
            "Name: count, dtype: int64\n",
            "Smallest cluster size: 1\n",
            "Largest cluster size: 12463\n",
            "Smallest cluster percentage: 0.004 %\n",
            "Largest cluster percentage: 52.682 %\n",
            "\n",
            "===== K = 9 =====\n",
            "0     1728\n",
            "1     3189\n",
            "2     2167\n",
            "3     1871\n",
            "4    12159\n",
            "5      525\n",
            "6      631\n",
            "7     1386\n",
            "8        1\n",
            "Name: count, dtype: int64\n",
            "Smallest cluster size: 1\n",
            "Largest cluster size: 12159\n",
            "Smallest cluster percentage: 0.004 %\n",
            "Largest cluster percentage: 51.397 %\n",
            "\n",
            "===== K = 10 =====\n",
            "0     1386\n",
            "1    10780\n",
            "2     1727\n",
            "3      583\n",
            "4     1619\n",
            "5     1863\n",
            "6        1\n",
            "7     1971\n",
            "8     3154\n",
            "9      573\n",
            "Name: count, dtype: int64\n",
            "Smallest cluster size: 1\n",
            "Largest cluster size: 10780\n",
            "Smallest cluster percentage: 0.004 %\n",
            "Largest cluster percentage: 45.568 %\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "    k  silhouette_score  smallest_cluster_size  largest_cluster_size  \\\n",
              "0   2             0.198                   6830                 16827   \n",
              "1   3             0.221                   2100                 16441   \n",
              "2   4             0.312                   1842                 17125   \n",
              "3   5             0.250                    526                 15683   \n",
              "4   6             0.290                    583                 13055   \n",
              "5   7             0.286                      1                 13701   \n",
              "6   8             0.315                      1                 12463   \n",
              "7   9             0.314                      1                 12159   \n",
              "8  10             0.350                      1                 10780   \n",
              "\n",
              "   smallest_cluster_pct  largest_cluster_pct  has_one_observation_cluster  \n",
              "0                28.871               71.129                        False  \n",
              "1                 8.877               69.497                        False  \n",
              "2                 7.786               72.389                        False  \n",
              "3                 2.223               66.293                        False  \n",
              "4                 2.464               55.185                        False  \n",
              "5                 0.004               57.915                         True  \n",
              "6                 0.004               52.682                         True  \n",
              "7                 0.004               51.397                         True  \n",
              "8                 0.004               45.568                         True  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-9d43db54-d42f-422c-840a-e6441f13c9ba\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>k</th>\n",
              "      <th>silhouette_score</th>\n",
              "      <th>smallest_cluster_size</th>\n",
              "      <th>largest_cluster_size</th>\n",
              "      <th>smallest_cluster_pct</th>\n",
              "      <th>largest_cluster_pct</th>\n",
              "      <th>has_one_observation_cluster</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2</td>\n",
              "      <td>0.198</td>\n",
              "      <td>6830</td>\n",
              "      <td>16827</td>\n",
              "      <td>28.871</td>\n",
              "      <td>71.129</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3</td>\n",
              "      <td>0.221</td>\n",
              "      <td>2100</td>\n",
              "      <td>16441</td>\n",
              "      <td>8.877</td>\n",
              "      <td>69.497</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4</td>\n",
              "      <td>0.312</td>\n",
              "      <td>1842</td>\n",
              "      <td>17125</td>\n",
              "      <td>7.786</td>\n",
              "      <td>72.389</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5</td>\n",
              "      <td>0.250</td>\n",
              "      <td>526</td>\n",
              "      <td>15683</td>\n",
              "      <td>2.223</td>\n",
              "      <td>66.293</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>6</td>\n",
              "      <td>0.290</td>\n",
              "      <td>583</td>\n",
              "      <td>13055</td>\n",
              "      <td>2.464</td>\n",
              "      <td>55.185</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>7</td>\n",
              "      <td>0.286</td>\n",
              "      <td>1</td>\n",
              "      <td>13701</td>\n",
              "      <td>0.004</td>\n",
              "      <td>57.915</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>8</td>\n",
              "      <td>0.315</td>\n",
              "      <td>1</td>\n",
              "      <td>12463</td>\n",
              "      <td>0.004</td>\n",
              "      <td>52.682</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>9</td>\n",
              "      <td>0.314</td>\n",
              "      <td>1</td>\n",
              "      <td>12159</td>\n",
              "      <td>0.004</td>\n",
              "      <td>51.397</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>10</td>\n",
              "      <td>0.350</td>\n",
              "      <td>1</td>\n",
              "      <td>10780</td>\n",
              "      <td>0.004</td>\n",
              "      <td>45.568</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9d43db54-d42f-422c-840a-e6441f13c9ba')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-9d43db54-d42f-422c-840a-e6441f13c9ba button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-9d43db54-d42f-422c-840a-e6441f13c9ba');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"display(cluster_decision_df\",\n  \"rows\": 9,\n  \"fields\": [\n    {\n      \"column\": \"k\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 2,\n        \"max\": 10,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          9,\n          3,\n          7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"silhouette_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.04939326314837322,\n        \"min\": 0.198,\n        \"max\": 0.35,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          0.314,\n          0.221,\n          0.286\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"smallest_cluster_size\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2218,\n        \"min\": 1,\n        \"max\": 6830,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          6830,\n          2100,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"largest_cluster_size\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2321,\n        \"min\": 10780,\n        \"max\": 17125,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          12159,\n          16441,\n          13701\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"smallest_cluster_pct\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9.377854917896261,\n        \"min\": 0.004,\n        \"max\": 28.871,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          28.871,\n          8.877,\n          0.004\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"largest_cluster_pct\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9.811004038833131,\n        \"min\": 45.568,\n        \"max\": 72.389,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          51.397,\n          69.497,\n          57.915\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"has_one_observation_cluster\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Cluster-Size Stability Interpretation\n",
        "\n",
        "The cluster-size comparison shows why the final cluster choice should not be based only on silhouette score.\n",
        "\n",
        "Although `K=10` produced the highest silhouette score, it also created a one-observation cluster. The same issue appeared for `K=7`, `K=8`, and `K=9`. These one-observation clusters are not useful as interpretable job-posting segments because they likely represent isolated outlier postings rather than meaningful groups.\n",
        "\n",
        "Among the stable alternatives, `K=4` had the strongest silhouette score, but its largest cluster contained over 72% of the training observations. This means the solution was still very dominated by one broad group. `K=5` also avoided one-observation clusters, but it had a lower silhouette score than `K=6` and still had a large dominant cluster.\n",
        "\n",
        "Therefore, `K=6` was selected as the final practical solution. It does not maximize silhouette score, but it provides a better balance between separation, cluster-size stability, and interpretability. The largest cluster falls to about 55% of the training data, while all clusters contain enough observations to be meaningful."
      ],
      "metadata": {
        "id": "Gn3HaMsz3vJH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 4.8 Set Final Number of Clusters\n",
        "\n",
        "optimal_k = 6\n",
        "\n",
        "print(\"Final selected K:\", optimal_k)\n",
        "\n",
        "selected_k_row = cluster_decision_df[\n",
        "    cluster_decision_df[\"k\"] == optimal_k\n",
        "]\n",
        "\n",
        "display(selected_k_row.round(3))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 98
        },
        "id": "o65YOlHZsCIi",
        "outputId": "0b97325a-cf68-4f8e-fbef-e818375d70f9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Final selected K: 6\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "   k  silhouette_score  smallest_cluster_size  largest_cluster_size  \\\n",
              "4  6              0.29                    583                 13055   \n",
              "\n",
              "   smallest_cluster_pct  largest_cluster_pct  has_one_observation_cluster  \n",
              "4                 2.464               55.185                        False  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-44538497-f39f-4922-943f-98c25138aa23\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>k</th>\n",
              "      <th>silhouette_score</th>\n",
              "      <th>smallest_cluster_size</th>\n",
              "      <th>largest_cluster_size</th>\n",
              "      <th>smallest_cluster_pct</th>\n",
              "      <th>largest_cluster_pct</th>\n",
              "      <th>has_one_observation_cluster</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>6</td>\n",
              "      <td>0.29</td>\n",
              "      <td>583</td>\n",
              "      <td>13055</td>\n",
              "      <td>2.464</td>\n",
              "      <td>55.185</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-44538497-f39f-4922-943f-98c25138aa23')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-44538497-f39f-4922-943f-98c25138aa23 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-44538497-f39f-4922-943f-98c25138aa23');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"display(selected_k_row\",\n  \"rows\": 1,\n  \"fields\": [\n    {\n      \"column\": \"k\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 6,\n        \"max\": 6,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"silhouette_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 0.29,\n        \"max\": 0.29,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.29\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"smallest_cluster_size\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 583,\n        \"max\": 583,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          583\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"largest_cluster_size\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 13055,\n        \"max\": 13055,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          13055\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"smallest_cluster_pct\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 2.464,\n        \"max\": 2.464,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          2.464\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"largest_cluster_pct\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 55.185,\n        \"max\": 55.185,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          55.185\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"has_one_observation_cluster\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##  Fit Final K-Means Model\n",
        "\n",
        "After selecting `K = 6`, I fit the final K-Means model on the full training clustering matrix.\n",
        "\n",
        "The model is fit only on the training clustering matrix. The test clustering matrix is then assigned clusters using `.predict()`. This avoids fitting the clustering model on the test data."
      ],
      "metadata": {
        "id": "cW8WGITPsZhp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 4.9 Fit Final K-Means Model and Assign Train/Test Clusters\n",
        "\n",
        "final_kmeans = KMeans(\n",
        "    n_clusters=optimal_k,\n",
        "    random_state=RANDOM_STATE,\n",
        "    n_init=10\n",
        ")\n",
        "\n",
        "train_clusters = final_kmeans.fit_predict(X_train_cluster_matrix)\n",
        "test_clusters = final_kmeans.predict(X_test_cluster_matrix)\n",
        "\n",
        "official_final_silhouette = silhouette_score(\n",
        "    X_train_cluster_matrix,\n",
        "    train_clusters\n",
        ")\n",
        "\n",
        "final_cluster_counts = pd.Series(train_clusters).value_counts().sort_index()\n",
        "\n",
        "print(\"Chosen number of clusters:\", optimal_k)\n",
        "print(\"Official full-training silhouette score:\", round(official_final_silhouette, 3))\n",
        "print(\"\\nFinal training cluster sizes:\")\n",
        "print(final_cluster_counts)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aC9T19pOsVEQ",
        "outputId": "0f9ac3ae-f397-4825-d837-1e1c143dfe29"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Chosen number of clusters: 6\n",
            "Official full-training silhouette score: 0.29\n",
            "\n",
            "Final training cluster sizes:\n",
            "0     4571\n",
            "1    13055\n",
            "2     1940\n",
            "3     1451\n",
            "4     2057\n",
            "5      583\n",
            "Name: count, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##  Final Silhouette Score Interpretation\n",
        "\n",
        "The final K-Means model with `K = 6` achieved a silhouette score of approximately 0.290 on the full training clustering matrix.\n",
        "\n",
        "This score suggests weak-to-moderate separation. That is expected in this project because job postings naturally overlap across description length, salary information, seniority, entry-level status, and role categories.\n",
        "\n",
        "The clusters should therefore be interpreted as broad job-posting segments, not perfectly separated categories. The purpose of clustering here is to summarize useful posting patterns and add cluster-based engineered features to the supervised models."
      ],
      "metadata": {
        "id": "bajnlG-Dseo4"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##  Add Cluster Features to the Supervised Modeling Data\n",
        "\n",
        "The final K-Means cluster labels are added to the supervised training and test datasets as engineered features.\n",
        "\n",
        "Because cluster labels are categories rather than ordered numbers, I one-hot encode them before using them in regression or classification models."
      ],
      "metadata": {
        "id": "hp7_-jk3slqr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 4.10 Add Cluster Features to Supervised Modeling Data\n",
        "\n",
        "X_train_fe = X_train.copy()\n",
        "X_test_fe = X_test.copy()\n",
        "\n",
        "X_train_fe[\"cluster_label\"] = train_clusters\n",
        "X_test_fe[\"cluster_label\"] = test_clusters\n",
        "\n",
        "X_train_cluster_dummies = pd.get_dummies(\n",
        "    X_train_fe[\"cluster_label\"],\n",
        "    prefix=\"cluster\"\n",
        ")\n",
        "\n",
        "X_test_cluster_dummies = pd.get_dummies(\n",
        "    X_test_fe[\"cluster_label\"],\n",
        "    prefix=\"cluster\"\n",
        ")\n",
        "\n",
        "X_train_cluster_dummies, X_test_cluster_dummies = X_train_cluster_dummies.align(\n",
        "    X_test_cluster_dummies,\n",
        "    join=\"left\",\n",
        "    axis=1,\n",
        "    fill_value=0\n",
        ")\n",
        "\n",
        "X_train_fe = X_train_fe.drop(columns=[\"cluster_label\"])\n",
        "X_test_fe = X_test_fe.drop(columns=[\"cluster_label\"])\n",
        "\n",
        "X_train_fe = pd.concat([X_train_fe, X_train_cluster_dummies], axis=1)\n",
        "X_test_fe = pd.concat([X_test_fe, X_test_cluster_dummies], axis=1)\n",
        "\n",
        "print(\"X_train_fe shape:\", X_train_fe.shape)\n",
        "print(\"X_test_fe shape:\", X_test_fe.shape)\n",
        "\n",
        "print(\"\\nCluster dummy columns added:\")\n",
        "print(list(X_train_cluster_dummies.columns))\n",
        "\n",
        "print(\"\\nTrain/test feature columns match:\")\n",
        "print(list(X_train_fe.columns) == list(X_test_fe.columns))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0X_pIRTwsjF7",
        "outputId": "a049c39b-a453-4b3f-c55d-68f2ea306374"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "X_train_fe shape: (23657, 30)\n",
            "X_test_fe shape: (5915, 30)\n",
            "\n",
            "Cluster dummy columns added:\n",
            "['cluster_0', 'cluster_1', 'cluster_2', 'cluster_3', 'cluster_4', 'cluster_5']\n",
            "\n",
            "Train/test feature columns match:\n",
            "True\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 4.11 Interactive K-Means Widget\n",
        "\n",
        "pca_kmeans = PCA(n_components=2, random_state=RANDOM_STATE)\n",
        "X_kmeans_2d_full = pca_kmeans.fit_transform(X_train_cluster_matrix)\n",
        "\n",
        "# Use a fixed sample only for plotting, not for scoring\n",
        "plot_sample_size = min(5000, X_train_cluster_matrix.shape[0])\n",
        "\n",
        "rng = np.random.default_rng(RANDOM_STATE)\n",
        "\n",
        "plot_sample_idx = rng.choice(\n",
        "    X_train_cluster_matrix.shape[0],\n",
        "    size=plot_sample_size,\n",
        "    replace=False\n",
        ")\n",
        "\n",
        "\n",
        "def plot_kmeans(k_val):\n",
        "    clear_output(wait=True)\n",
        "\n",
        "    kmeans_interactive = KMeans(\n",
        "        n_clusters=k_val,\n",
        "        random_state=RANDOM_STATE,\n",
        "        n_init=10\n",
        "    )\n",
        "\n",
        "    # Fit on the FULL training clustering matrix\n",
        "    full_labels = kmeans_interactive.fit_predict(X_train_cluster_matrix)\n",
        "\n",
        "    # Score on the FULL training clustering matrix\n",
        "    full_silhouette = silhouette_score(\n",
        "        X_train_cluster_matrix,\n",
        "        full_labels\n",
        "    )\n",
        "\n",
        "    # Use only sampled points for plotting\n",
        "    plot_labels = full_labels[plot_sample_idx]\n",
        "\n",
        "    plt.figure(figsize=(10, 7))\n",
        "\n",
        "    plt.scatter(\n",
        "        X_kmeans_2d_full[plot_sample_idx, 0],\n",
        "        X_kmeans_2d_full[plot_sample_idx, 1],\n",
        "        c=plot_labels,\n",
        "        alpha=0.65\n",
        "    )\n",
        "\n",
        "    plt.title(\n",
        "        f\"K-Means Clustering with K = {k_val} | \"\n",
        "        f\"Full-Data Silhouette Score = {full_silhouette:.3f}\"\n",
        "    )\n",
        "    plt.xlabel(\"PCA Component 1\")\n",
        "    plt.ylabel(\"PCA Component 2\")\n",
        "    plt.grid(alpha=0.3)\n",
        "    plt.show()\n",
        "\n",
        "\n",
        "k_slider = widgets.IntSlider(\n",
        "    min=2,\n",
        "    max=10,\n",
        "    step=1,\n",
        "    value=optimal_k,\n",
        "    description=\"K:\",\n",
        "    continuous_update=False\n",
        ")\n",
        "\n",
        "interactive_plot = widgets.interactive(\n",
        "    plot_kmeans,\n",
        "    k_val=k_slider\n",
        ")\n",
        "\n",
        "display(interactive_plot)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 673,
          "referenced_widgets": [
            "579652d9958f45eeaa349b3db0c0e0c7",
            "335375d0f7354f72b3005c90653be503",
            "6e756e10ae0d48c0a37c14b6bc2c40b8",
            "ee875dd930564f88b74761b5eababa5d",
            "57f20138b1284b969ff07688f4cbf608",
            "5ba6c3a562e64422ac3c415ff7b813aa",
            "b4f01d8011614ca590aca005171571c4"
          ]
        },
        "id": "W8mQ_skFs0MU",
        "outputId": "3e210f44-6b22-47b4-be32-e2b41945e441"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "interactive(children=(IntSlider(value=6, continuous_update=False, description='K:', max=10, min=2), Output()),…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "579652d9958f45eeaa349b3db0c0e0c7"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Interactive K-Means Widget\n",
        "\n",
        "The interactive K-Means widget allows different values of `K` to be explored visually.\n",
        "\n",
        "This widget is a visualization tool, not the final selection rule. The final `K = 6` decision was already made using the full-data silhouette scores and the cluster-size stability table.\n",
        "\n",
        "For speed and readability, the widget uses a fixed sample from the training clustering matrix. Therefore, the exact silhouette scores shown in the widget may differ slightly from the official full-training silhouette scores above."
      ],
      "metadata": {
        "id": "4S18oMXFsuCM"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Hierarchical Clustering Dendrogram\n",
        "\n",
        "The dendrogram provides a hierarchical view of the clustering structure.\n",
        "\n",
        "This is included as a complementary visualization, not as the final method for selecting `K`. Hierarchical clustering is computationally expensive, so this dendrogram uses a fixed sample of 300 observations from the training clustering matrix.\n",
        "\n",
        "Because this method uses a smaller sample and a different clustering algorithm, its result should be interpreted as visual support rather than a direct replacement for the K-Means decision."
      ],
      "metadata": {
        "id": "nJhKfLm5t7--"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 4.12 Hierarchical Clustering Dendrogram\n",
        "\n",
        "hier_sample_size = min(300, X_train_cluster_matrix.shape[0])\n",
        "\n",
        "rng = np.random.default_rng(RANDOM_STATE)\n",
        "\n",
        "hier_sample_idx = rng.choice(\n",
        "    X_train_cluster_matrix.shape[0],\n",
        "    size=hier_sample_size,\n",
        "    replace=False\n",
        ")\n",
        "\n",
        "X_hier_sample = X_train_cluster_matrix[hier_sample_idx]\n",
        "\n",
        "linked = linkage(X_hier_sample, method=\"ward\")\n",
        "\n",
        "plt.figure(figsize=(14, 7))\n",
        "\n",
        "dendrogram(\n",
        "    linked,\n",
        "    orientation=\"top\",\n",
        "    distance_sort=\"descending\",\n",
        "    show_leaf_counts=True,\n",
        "    truncate_mode=\"lastp\",\n",
        "    p=30\n",
        ")\n",
        "\n",
        "plt.title(\"Hierarchical Clustering Dendrogram on Fixed Sample using Ward Linkage\")\n",
        "plt.xlabel(\"Clustered Sample Groups\")\n",
        "plt.ylabel(\"Distance / Ward Linkage\")\n",
        "\n",
        "plt.axhline(\n",
        "    y=20,\n",
        "    linestyle=\"--\",\n",
        "    label=\"Illustrative Cut-off Line\"\n",
        ")\n",
        "\n",
        "plt.legend()\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 707
        },
        "id": "RapPss6ht2d9",
        "outputId": "40025d7b-d39d-4f9c-99d3-0a640b4871f3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x700 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Dendrogram Interpretation\n",
        "\n",
        "The dendrogram provides a hierarchical view of the structure in a fixed sample of the training clustering data. It shows how sampled job postings are progressively merged into larger groups using Ward linkage.\n",
        "\n",
        "This visualization suggests that the data contains some broad grouping structure, because several groups merge only at higher linkage distances. However, the dendrogram is based on a small sample and uses hierarchical clustering rather than the final K-Means model.\n",
        "\n",
        "Therefore, I do not use the dendrogram as the final rule for selecting `K`. The final cluster choice is based primarily on the full-data K-Means silhouette analysis and the cluster-size stability comparison.\n",
        "\n",
        "In this project, the dendrogram is used as a complementary visual diagnostic: it supports the idea that grouping structure exists, but it does not override the final decision to use `K = 6`."
      ],
      "metadata": {
        "id": "9fx3DmGauXi2"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Agglomerative Clustering Diagnostic Comparison\n",
        "\n",
        "After selecting the final K-Means solution using the full training clustering matrix, I added Agglomerative Clustering as a diagnostic comparison.\n",
        "\n",
        "Agglomerative Clustering is hierarchy-based, while K-Means is centroid-based. Because Agglomerative Clustering is computationally expensive on the full training matrix, I evaluate it on the same fixed sample used for the interactive visualizations.\n",
        "\n",
        "This section is not used to override the final K-Means choice. Its purpose is only to check whether a different clustering method also finds broad grouping structure in the data."
      ],
      "metadata": {
        "id": "9G0TAuf9wGVU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 4.13 Agglomerative Clustering Diagnostic Comparison\n",
        "\n",
        "agg_results = []\n",
        "\n",
        "for k in range(2, 11):\n",
        "    agg_sample = AgglomerativeClustering(\n",
        "        n_clusters=k,\n",
        "        metric=\"euclidean\",\n",
        "        linkage=\"ward\"\n",
        "    )\n",
        "\n",
        "    agg_sample_labels = agg_sample.fit_predict(X_interactive_eval)\n",
        "\n",
        "    agg_sample_score = silhouette_score(\n",
        "        X_interactive_eval,\n",
        "        agg_sample_labels\n",
        "    )\n",
        "\n",
        "    agg_results.append({\n",
        "        \"model\": \"Agglomerative sampled data\",\n",
        "        \"k\": k,\n",
        "        \"sample_silhouette_score\": agg_sample_score\n",
        "    })\n",
        "\n",
        "agg_comparison_df = pd.DataFrame(agg_results)\n",
        "\n",
        "display(agg_comparison_df.round(3))\n",
        "\n",
        "plt.figure(figsize=(9, 5))\n",
        "plt.plot(\n",
        "    agg_comparison_df[\"k\"],\n",
        "    agg_comparison_df[\"sample_silhouette_score\"],\n",
        "    marker=\"o\"\n",
        ")\n",
        "\n",
        "plt.title(\"Agglomerative Clustering Diagnostic Comparison\")\n",
        "plt.xlabel(\"Number of Clusters (K)\")\n",
        "plt.ylabel(\"Sample Silhouette Score\")\n",
        "plt.xticks(range(2, 11))\n",
        "plt.grid(alpha=0.3)\n",
        "plt.show()\n",
        "\n",
        "best_agg_result = agg_comparison_df.sort_values(\n",
        "    \"sample_silhouette_score\",\n",
        "    ascending=False\n",
        ").head(1)\n",
        "\n",
        "print(\"Best sampled Agglomerative result:\")\n",
        "display(best_agg_result.round(3))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 883
        },
        "id": "_XM8Idebvrh8",
        "outputId": "3f4e03d5-7cdc-47cd-e623-d7fca17b9f3f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                        model   k  sample_silhouette_score\n",
              "0  Agglomerative sampled data   2                    0.467\n",
              "1  Agglomerative sampled data   3                    0.338\n",
              "2  Agglomerative sampled data   4                    0.314\n",
              "3  Agglomerative sampled data   5                    0.333\n",
              "4  Agglomerative sampled data   6                    0.346\n",
              "5  Agglomerative sampled data   7                    0.365\n",
              "6  Agglomerative sampled data   8                    0.329\n",
              "7  Agglomerative sampled data   9                    0.332\n",
              "8  Agglomerative sampled data  10                    0.308"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-40d616ba-c0c1-4926-91c3-53989c9c55fc\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>model</th>\n",
              "      <th>k</th>\n",
              "      <th>sample_silhouette_score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Agglomerative sampled data</td>\n",
              "      <td>2</td>\n",
              "      <td>0.467</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Agglomerative sampled data</td>\n",
              "      <td>3</td>\n",
              "      <td>0.338</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Agglomerative sampled data</td>\n",
              "      <td>4</td>\n",
              "      <td>0.314</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Agglomerative sampled data</td>\n",
              "      <td>5</td>\n",
              "      <td>0.333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Agglomerative sampled data</td>\n",
              "      <td>6</td>\n",
              "      <td>0.346</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Agglomerative sampled data</td>\n",
              "      <td>7</td>\n",
              "      <td>0.365</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Agglomerative sampled data</td>\n",
              "      <td>8</td>\n",
              "      <td>0.329</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Agglomerative sampled data</td>\n",
              "      <td>9</td>\n",
              "      <td>0.332</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Agglomerative sampled data</td>\n",
              "      <td>10</td>\n",
              "      <td>0.308</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-40d616ba-c0c1-4926-91c3-53989c9c55fc')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-40d616ba-c0c1-4926-91c3-53989c9c55fc button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-40d616ba-c0c1-4926-91c3-53989c9c55fc');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"display(best_agg_result\",\n  \"rows\": 9,\n  \"fields\": [\n    {\n      \"column\": \"model\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Agglomerative sampled data\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"k\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 2,\n        \"max\": 10,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          9\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sample_silhouette_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.04763402145525822,\n        \"min\": 0.308,\n        \"max\": 0.467,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          0.332\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 900x500 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best sampled Agglomerative result:\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                        model  k  sample_silhouette_score\n",
              "0  Agglomerative sampled data  2                    0.467"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b69cb0eb-08b6-4512-a40e-6e166a04957c\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>model</th>\n",
              "      <th>k</th>\n",
              "      <th>sample_silhouette_score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Agglomerative sampled data</td>\n",
              "      <td>2</td>\n",
              "      <td>0.467</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b69cb0eb-08b6-4512-a40e-6e166a04957c')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-b69cb0eb-08b6-4512-a40e-6e166a04957c button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-b69cb0eb-08b6-4512-a40e-6e166a04957c');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"display(best_agg_result\",\n  \"rows\": 1,\n  \"fields\": [\n    {\n      \"column\": \"model\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Agglomerative sampled data\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"k\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 2,\n        \"max\": 2,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sample_silhouette_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 0.467,\n        \"max\": 0.467,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.467\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Agglomerative Diagnostic Interpretation\n",
        "\n",
        "The Agglomerative Clustering diagnostic was used as a secondary comparison method. Unlike K-Means, which creates centroid-based clusters, Agglomerative Clustering builds a hierarchy by progressively merging similar observations.\n",
        "\n",
        "On the fixed sample, Agglomerative Clustering achieved its strongest silhouette score at `K = 2`, with a score of approximately **0.467**. This suggests that the sampled data contains one strong broad separation between two groups of job postings.\n",
        "\n",
        "However, `K = 2` is too coarse for the segmentation goal of this project. Among the richer Agglomerative solutions, `K = 7` produced the strongest score, but this result is sample-based and does not override the final K-Means selection.\n",
        "\n",
        "Overall, this diagnostic supports the idea that meaningful grouping structure exists in the data, but it also shows that the preferred number of clusters can depend on the clustering method and evaluation setup. Therefore, the final clustering decision remains based on the full-data K-Means silhouette analysis, cluster-size stability, and interpretability.\n",
        "At teh end of the day, Agglomerative is a diagnostic comparison showing broad structure, while K=6 is selected from the official K-Means stability analysis."
      ],
      "metadata": {
        "id": "mULQjV4QwMgC"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##  PCA Visualization of Final K-Means Clusters\n",
        "\n",
        "To visualize the final selected K-Means model, I project the training clustering matrix into two dimensions using PCA.\n",
        "\n",
        "This plot is only for interpretation. The final K-Means model was fit on the full 12-feature clustering matrix, not on the two PCA components."
      ],
      "metadata": {
        "id": "wZTqjIBrwpQ0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        " #4.14 PCA Visualization of Final K-Means Clusters\n",
        "\n",
        "pca_final = PCA(n_components=2, random_state=RANDOM_STATE)\n",
        "X_train_cluster_2d = pca_final.fit_transform(X_train_cluster_matrix)\n",
        "\n",
        "pca_plot_df = pd.DataFrame({\n",
        "    \"PC1\": X_train_cluster_2d[:, 0],\n",
        "    \"PC2\": X_train_cluster_2d[:, 1],\n",
        "    \"cluster\": train_clusters\n",
        "})\n",
        "\n",
        "plt.figure(figsize=(9, 7))\n",
        "\n",
        "sns.scatterplot(\n",
        "    data=pca_plot_df.sample(\n",
        "        n=min(5000, len(pca_plot_df)),\n",
        "        random_state=RANDOM_STATE\n",
        "    ),\n",
        "    x=\"PC1\",\n",
        "    y=\"PC2\",\n",
        "    hue=\"cluster\",\n",
        "    palette=\"tab10\",\n",
        "    alpha=0.5\n",
        ")\n",
        "\n",
        "plt.title(f\"Final K-Means Clusters Visualized with PCA, K = {optimal_k}\")\n",
        "plt.xlabel(\"PCA Component 1\")\n",
        "plt.ylabel(\"PCA Component 2\")\n",
        "plt.legend(title=\"Cluster\")\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 707
        },
        "id": "mozmz31qwsVn",
        "outputId": "9220ea9f-87ea-4670-c518-7c2cd8983851"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 900x700 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### PCA Cluster Visualization Interpretation\n",
        "\n",
        "The PCA visualization shows the final K-Means clustering solution with `K = 6` projected into two dimensions.\n",
        "\n",
        "The plot shows that the clusters are not perfectly separated. Several clusters overlap, especially in the main diagonal bands on the right side of the plot. This is consistent with the final silhouette score, which indicated weak-to-moderate separation.\n",
        "\n",
        "At the same time, the visualization shows some meaningful structure. One cluster appears more clearly separated from the rest, while the remaining clusters form partially distinct but overlapping posting profiles.\n",
        "\n",
        "This supports the interpretation that the clusters should be treated as broad job-posting segments rather than clean categories. The purpose of clustering in this project is to capture additional structure in the posting features and use it as engineered information for the supervised models, not to claim that every posting belongs to a perfectly distinct group."
      ],
      "metadata": {
        "id": "OWcKTto0xOcC"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##  Cluster Profile Table\n",
        "\n",
        "The cluster profile table helps interpret what each cluster represents.\n",
        "\n",
        "The clusters were created using posting features only. The target variable was not used when fitting K-Means. The average `views` value is added afterward only for interpretation."
      ],
      "metadata": {
        "id": "kiWfL_-ow-_V"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 4.15 Cluster Profile Table\n",
        "\n",
        "cluster_profile = X_train.copy()\n",
        "cluster_profile[\"cluster_label\"] = train_clusters\n",
        "cluster_profile[\"views\"] = np.expm1(y_train)\n",
        "\n",
        "summary_cols = [\n",
        "    col for col in cluster_features\n",
        "    if col in cluster_profile.columns\n",
        "] + [\"views\"]\n",
        "\n",
        "cluster_summary = cluster_profile.groupby(\"cluster_label\")[summary_cols].mean()\n",
        "cluster_sizes = cluster_profile[\"cluster_label\"].value_counts().sort_index()\n",
        "\n",
        "print(\"Training cluster sizes:\")\n",
        "print(cluster_sizes)\n",
        "\n",
        "display(cluster_summary.round(2))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 425
        },
        "id": "RdWTRUlfxD3G",
        "outputId": "50d0cb07-d033-43d9-ea64-665b43b105a8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training cluster sizes:\n",
            "cluster_label\n",
            "0     4571\n",
            "1    13055\n",
            "2     1940\n",
            "3     1451\n",
            "4     2057\n",
            "5      583\n",
            "Name: count, dtype: int64\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "               title_word_count  description_word_count  salary_log  \\\n",
              "cluster_label                                                         \n",
              "0                          4.27                  602.04       11.68   \n",
              "1                          4.32                  504.88       11.39   \n",
              "2                          4.09                  483.95        3.54   \n",
              "3                          4.39                  551.41        9.58   \n",
              "4                          3.88                  499.07       11.56   \n",
              "5                          4.38                  530.95        9.49   \n",
              "\n",
              "               description_density  has_salary_info  is_senior_role  \\\n",
              "cluster_label                                                         \n",
              "0                             7.30             0.27            0.20   \n",
              "1                             7.17             0.19            0.06   \n",
              "2                             7.18             1.00            0.07   \n",
              "3                             7.31             0.33            0.27   \n",
              "4                             7.47             0.23            0.27   \n",
              "5                             7.23             0.30            0.12   \n",
              "\n",
              "               is_entry_role  is_software_role  is_data_role  is_manager_role  \\\n",
              "cluster_label                                                                   \n",
              "0                       0.01              0.00          0.00             1.00   \n",
              "1                       0.03              0.00          0.00             0.00   \n",
              "2                       0.04              0.08          0.00             0.11   \n",
              "3                       0.04              0.13          1.00             0.12   \n",
              "4                       0.03              1.00          0.00             0.07   \n",
              "5                       0.10              0.02          0.05             0.42   \n",
              "\n",
              "               is_sales_role  is_marketing_role  views  \n",
              "cluster_label                                           \n",
              "0                       0.11                0.0  15.50  \n",
              "1                       0.09                0.0  11.51  \n",
              "2                       0.04                0.0  15.32  \n",
              "3                       0.02                0.0  32.10  \n",
              "4                       0.01                0.0  22.56  \n",
              "5                       0.08                1.0  32.46  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-0b235dd6-0f06-4032-bb16-b6c9c6ae59b7\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>title_word_count</th>\n",
              "      <th>description_word_count</th>\n",
              "      <th>salary_log</th>\n",
              "      <th>description_density</th>\n",
              "      <th>has_salary_info</th>\n",
              "      <th>is_senior_role</th>\n",
              "      <th>is_entry_role</th>\n",
              "      <th>is_software_role</th>\n",
              "      <th>is_data_role</th>\n",
              "      <th>is_manager_role</th>\n",
              "      <th>is_sales_role</th>\n",
              "      <th>is_marketing_role</th>\n",
              "      <th>views</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>cluster_label</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>4.27</td>\n",
              "      <td>602.04</td>\n",
              "      <td>11.68</td>\n",
              "      <td>7.30</td>\n",
              "      <td>0.27</td>\n",
              "      <td>0.20</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.11</td>\n",
              "      <td>0.0</td>\n",
              "      <td>15.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4.32</td>\n",
              "      <td>504.88</td>\n",
              "      <td>11.39</td>\n",
              "      <td>7.17</td>\n",
              "      <td>0.19</td>\n",
              "      <td>0.06</td>\n",
              "      <td>0.03</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.09</td>\n",
              "      <td>0.0</td>\n",
              "      <td>11.51</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4.09</td>\n",
              "      <td>483.95</td>\n",
              "      <td>3.54</td>\n",
              "      <td>7.18</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.07</td>\n",
              "      <td>0.04</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.11</td>\n",
              "      <td>0.04</td>\n",
              "      <td>0.0</td>\n",
              "      <td>15.32</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.39</td>\n",
              "      <td>551.41</td>\n",
              "      <td>9.58</td>\n",
              "      <td>7.31</td>\n",
              "      <td>0.33</td>\n",
              "      <td>0.27</td>\n",
              "      <td>0.04</td>\n",
              "      <td>0.13</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.12</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0.0</td>\n",
              "      <td>32.10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3.88</td>\n",
              "      <td>499.07</td>\n",
              "      <td>11.56</td>\n",
              "      <td>7.47</td>\n",
              "      <td>0.23</td>\n",
              "      <td>0.27</td>\n",
              "      <td>0.03</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.07</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.0</td>\n",
              "      <td>22.56</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>4.38</td>\n",
              "      <td>530.95</td>\n",
              "      <td>9.49</td>\n",
              "      <td>7.23</td>\n",
              "      <td>0.30</td>\n",
              "      <td>0.12</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0.05</td>\n",
              "      <td>0.42</td>\n",
              "      <td>0.08</td>\n",
              "      <td>1.0</td>\n",
              "      <td>32.46</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0b235dd6-0f06-4032-bb16-b6c9c6ae59b7')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-0b235dd6-0f06-4032-bb16-b6c9c6ae59b7 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-0b235dd6-0f06-4032-bb16-b6c9c6ae59b7');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"display(cluster_summary\",\n  \"rows\": 6,\n  \"fields\": [\n    {\n      \"column\": \"cluster_label\",\n      \"properties\": {\n        \"dtype\": \"int32\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          0,\n          1,\n          5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"title_word_count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.1997414996105383,\n        \"min\": 3.88,\n        \"max\": 4.39,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          4.27,\n          4.32,\n          4.38\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"description_word_count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 43.23760477485618,\n        \"min\": 483.95,\n        \"max\": 602.04,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          602.04,\n          504.88,\n          530.95\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"salary_log\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3.1011804204205857,\n        \"min\": 3.54,\n        \"max\": 11.68,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          11.68,\n          11.39,\n          9.49\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"description_density\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.11129540272026804,\n        \"min\": 7.17,\n        \"max\": 7.47,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          7.3,\n          7.17,\n          7.23\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"has_salary_info\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3045433740317899,\n        \"min\": 0.19,\n        \"max\": 1.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          0.27,\n          0.19,\n          0.3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"is_senior_role\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.09523654760647302,\n        \"min\": 0.06,\n        \"max\": 0.27,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.06,\n          0.12,\n          0.07\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"is_entry_role\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.03060501048303475,\n        \"min\": 0.01,\n        \"max\": 0.1,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.03,\n          0.1,\n          0.01\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"is_software_role\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.39282311540946774,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.08,\n          0.02,\n          0.13\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"is_data_role\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4046603514059661,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.0,\n          1.0,\n          0.05\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"is_manager_role\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.37808288332939205,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          1.0,\n          0.0,\n          0.42\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"is_sales_role\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.04070217029430576,\n        \"min\": 0.01,\n        \"max\": 0.11,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          0.11,\n          0.09,\n          0.08\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"is_marketing_role\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.408248290463863,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1.0,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"views\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9.028443387428423,\n        \"min\": 11.51,\n        \"max\": 32.46,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          15.5,\n          11.51\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##  Cluster Profile Interpretation\n",
        "\n",
        "The cluster profile table helps explain what the final `K = 6` K-Means solution captured.\n",
        "\n",
        "Based on the cluster profile table:\n",
        "\n",
        "- **Cluster 0** appears to represent **manager-focused postings**. This cluster has `is_manager_role = 1.00`, the longest average descriptions, and a relatively high salary signal. However, its average views are not among the highest, suggesting that management-related structure alone does not guarantee high engagement.\n",
        "\n",
        "- **Cluster 1** appears to represent **general or mixed postings**. It is the largest cluster and does not have a strong software, data, manager, sales, or marketing role signal. It also has the lowest average views, suggesting that less specialized postings may receive weaker engagement.\n",
        "\n",
        "- **Cluster 2** appears to represent **salary-transparent postings**. This cluster has `has_salary_info = 1.00`, meaning all postings in the cluster include salary information. However, its average views are only moderate, suggesting that salary transparency may be useful but is not enough by itself to create high engagement.\n",
        "\n",
        "- **Cluster 3** appears to represent **data-role postings**. This cluster has `is_data_role = 1.00` and one of the highest average view levels. This suggests that data-related postings may be associated with stronger engagement in the training data.\n",
        "\n",
        "- **Cluster 4** appears to represent **software-role postings**. This cluster has `is_software_role = 1.00`, relatively high description density, and moderate-to-high average views compared with the general cluster.\n",
        "\n",
        "- **Cluster 5** appears to represent **marketing-focused postings with some management overlap**. This cluster has `is_marketing_role = 1.00` and a relatively high `is_manager_role` value. It has the highest average views among the clusters, suggesting that this posting profile may be associated with stronger engagement in the training data.\n",
        "\n",
        "The largest cluster is Cluster 1, which appears to represent general or mixed postings without a strong role-category signal. It also has the lowest average views, suggesting that less specialized postings may receive weaker engagement.\n",
        "\n",
        "Several clusters are clearly defined by role-category indicators. Cluster 0 is manager-focused, Cluster 3 is data-focused, Cluster 4 is software-focused, and Cluster 5 is marketing-focused with some management overlap. Cluster 2 is mainly defined by salary transparency, since all postings in that cluster include salary information.\n",
        "\n",
        "The highest average views appear in Cluster 5 and Cluster 3, suggesting that marketing-related and data-related postings may be associated with stronger engagement in the training data. However, these averages should be interpreted cautiously because views are not used to create the clusters and may be influenced by unobserved factors such as company reputation, platform ranking, and paid promotion.\n",
        "\n",
        "Overall, these clusters should be interpreted as broad posting profiles rather than clean categories. They are useful because they summarize combinations of role type, salary signal, seniority, and text structure that may not be fully captured by individual features alone.\n",
        "\n",
        "The cluster-level view also shows an important limitation: salary transparency alone does not automatically lead to the highest engagement. Cluster 2 is fully salary-transparent but does not have the highest average views. This suggests that salary information may matter together with other factors, such as role type, description quality, and posting context."
      ],
      "metadata": {
        "id": "1V7gy3TZxtm0"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##  Confirm Cluster Matrix Rebuild from Training Features\n",
        "\n",
        "This final check confirms that the clustering matrix can be reconstructed from the training features using the same selected clustering columns and the same fitted preprocessing logic.\n",
        "\n",
        "This prevents inconsistencies between the clustering analysis and the final engineered cluster features."
      ],
      "metadata": {
        "id": "vWtGDqqGyMXn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 4.16 Final Modeling Matrix Sanity Check\n",
        "\n",
        "print(\"X_train_fe shape:\", X_train_fe.shape)\n",
        "print(\"X_test_fe shape:\", X_test_fe.shape)\n",
        "\n",
        "print(\"Missing values in y_train:\", y_train.isna().sum())\n",
        "print(\"Missing values in y_test:\", y_test.isna().sum())\n",
        "\n",
        "print(\"Train/test feature columns match:\", list(X_train_fe.columns) == list(X_test_fe.columns))\n",
        "\n",
        "cluster_dummy_cols = [\n",
        "    col for col in X_train_fe.columns\n",
        "    if col.startswith(\"cluster_\")\n",
        "]\n",
        "\n",
        "print(\"\\nCluster dummy columns:\")\n",
        "print(cluster_dummy_cols)\n",
        "\n",
        "print(\"\\nNumber of cluster dummy columns:\", len(cluster_dummy_cols))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SRE4QsmfyPph",
        "outputId": "8db43797-73d7-4ba0-e0e1-081aaf010734"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "X_train_fe shape: (23657, 30)\n",
            "X_test_fe shape: (5915, 30)\n",
            "Missing values in y_train: 0\n",
            "Missing values in y_test: 0\n",
            "Train/test feature columns match: True\n",
            "\n",
            "Cluster dummy columns:\n",
            "['cluster_0', 'cluster_1', 'cluster_2', 'cluster_3', 'cluster_4', 'cluster_5']\n",
            "\n",
            "Number of cluster dummy columns: 6\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 4.23 Confirm Cluster Matrix Rebuild from Training Features\n",
        "\n",
        "X_train_cluster_matrix_check = cluster_preprocessor.transform(\n",
        "    X_train[cluster_features]\n",
        ")\n",
        "\n",
        "X_test_cluster_matrix_check = cluster_preprocessor.transform(\n",
        "    X_test[cluster_features]\n",
        ")\n",
        "\n",
        "print(\"Original training cluster matrix shape:\", X_train_cluster_matrix.shape)\n",
        "print(\"Rebuilt training cluster matrix shape:\", X_train_cluster_matrix_check.shape)\n",
        "\n",
        "print(\"Original test cluster matrix shape:\", X_test_cluster_matrix.shape)\n",
        "print(\"Rebuilt test cluster matrix shape:\", X_test_cluster_matrix_check.shape)\n",
        "\n",
        "print(\n",
        "    \"Training matrix consistent:\",\n",
        "    np.allclose(X_train_cluster_matrix, X_train_cluster_matrix_check)\n",
        ")\n",
        "\n",
        "print(\n",
        "    \"Test matrix consistent:\",\n",
        "    np.allclose(X_test_cluster_matrix, X_test_cluster_matrix_check)\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R4qrtklQyaDl",
        "outputId": "607b2fe1-32a0-4fd9-83b3-716fd08294a7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Original training cluster matrix shape: (23657, 12)\n",
            "Rebuilt training cluster matrix shape: (23657, 12)\n",
            "Original test cluster matrix shape: (5915, 12)\n",
            "Rebuilt test cluster matrix shape: (5915, 12)\n",
            "Training matrix consistent: True\n",
            "Test matrix consistent: True\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pykMu4YaXHEx"
      },
      "source": [
        "# **Part 5: Train and Evaluate Three Improved Models**\n",
        "\n",
        "* Retrain your `Linear Regression` model with the engineered features.\n",
        "* Choose and Train two different types on models from the SKlearn package (DOCS), on the engineered dataset.\n",
        "* Compare performance with your baseline.\n",
        "* Visualize feature importance.\n",
        "* <u>Discuss the improvement and the reasons.</u>\n",
        "* Declare the winner."
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Improved Regression Models\n",
        "\n",
        "After adding advanced interaction features and clustering-based features, I train additional regression models to test whether predictive performance improves beyond the baseline Linear Regression model.\n",
        "\n",
        "The models are trained using `X_train_fe` and evaluated on `X_test_fe`, which include the final engineered feature set and the KMeans cluster features.\n",
        "\n",
        "The models tested in this stage are:\n",
        "\n",
        "1. **Linear Regression + Engineered Features** — checks whether the expanded feature set improves the linear baseline.\n",
        "2. **Random Forest Regressor** — captures non-linear relationships and feature interactions.\n",
        "3. **Gradient Boosting Regressor** — builds sequential trees to improve predictive accuracy."
      ],
      "metadata": {
        "id": "OHyggN3VoDaZ"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FPNSRMA8m3Ii",
        "outputId": "59f940d1-e209-4930-ce92-85226e2192f6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Linear Regression + Engineered Features\n",
            "---- Log scale (model evaluation scale) ----\n",
            "MAE_log: 0.5997\n",
            "MSE_log: 0.7096\n",
            "RMSE_log: 0.8424\n",
            "R²: 0.0640\n",
            "---- Original scale (business interpretation) ----\n",
            "MAE_views: 10.54\n",
            "MSE_views: 8016.98\n",
            "RMSE_views: 89.54\n"
          ]
        }
      ],
      "source": [
        "# 5.1 Linear Regression with Engineered Features\n",
        "\n",
        "linear_fe_model = Pipeline(steps=[\n",
        "    (\"preprocessor\", preprocessor),\n",
        "    (\"model\", LinearRegression())\n",
        "])\n",
        "\n",
        "linear_fe_model.fit(X_train_fe, y_train)\n",
        "\n",
        "linear_fe_preds = linear_fe_model.predict(X_test_fe)\n",
        "\n",
        "results.append(\n",
        "    evaluate_regression(\n",
        "        \"Linear Regression + Engineered Features\",\n",
        "        y_test,\n",
        "        linear_fe_preds\n",
        "    )\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Linear Regression with Engineered Features Interpretation\n",
        "\n",
        "The Linear Regression model with engineered features achieved an **R² of 0.064** and an **RMSE_log of 0.842**. This indicates that the model explains only a small portion of the variation in job-post views. Although feature engineering added variables such as word counts, salary-based features, and interaction terms, these features did not substantially improve the performance of a purely linear model.\n",
        "\n",
        "The result suggests that the relationship between job-post characteristics and engagement is likely non-linear. Some features may only matter under certain conditions, such as salary being more relevant for senior roles or description length helping only up to a certain point. Therefore, the engineered features may be more useful in non-linear models such as Random Forest or Gradient Boosting, which can capture interactions and threshold effects more effectively.\n",
        "\n",
        "The large gap between MAE_views and RMSE_views also suggests that the model makes several large prediction errors, probably on job posts with unusually high view counts. Overall, the Linear Regression model appears to underfit the data and should mainly be used as a baseline comparison for stronger models."
      ],
      "metadata": {
        "id": "0FkGX15enabA"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e_iSfIva2EK4",
        "outputId": "cb760805-b8bc-4c6e-c91b-09653656fde8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Random Forest\n",
            "---- Log scale (model evaluation scale) ----\n",
            "MAE_log: 0.5923\n",
            "MSE_log: 0.6970\n",
            "RMSE_log: 0.8349\n",
            "R²: 0.0807\n",
            "---- Original scale (business interpretation) ----\n",
            "MAE_views: 10.47\n",
            "MSE_views: 8009.99\n",
            "RMSE_views: 89.50\n",
            "Random Forest train R²: 0.12598304573064434\n",
            "Random Forest test R²: 0.08066399837579763\n",
            "Random Forest train RMSE_log: 0.8496613991756675\n",
            "Random Forest test RMSE_log: 0.8348766256576109\n"
          ]
        }
      ],
      "source": [
        "# 5.2 Random Forest Regressor\n",
        "\n",
        "rf_model = Pipeline(steps=[\n",
        "    (\"preprocessor\", preprocessor),\n",
        "    (\"model\", RandomForestRegressor(\n",
        "        n_estimators=300,\n",
        "        max_depth=8,\n",
        "        min_samples_split=20,\n",
        "        min_samples_leaf=10,\n",
        "        max_features=\"sqrt\",\n",
        "        random_state=RANDOM_STATE,\n",
        "        n_jobs=-1\n",
        "    ))\n",
        "])\n",
        "\n",
        "rf_model.fit(X_train_fe, y_train)\n",
        "\n",
        "rf_preds = rf_model.predict(X_test_fe)\n",
        "\n",
        "results.append(\n",
        "    evaluate_regression(\n",
        "        \"Random Forest\",\n",
        "        y_test,\n",
        "        rf_preds\n",
        "    )\n",
        ")\n",
        "\n",
        "# Diagnostic check\n",
        "rf_train_preds = rf_model.predict(X_train_fe)\n",
        "rf_test_preds = rf_model.predict(X_test_fe)\n",
        "\n",
        "print(\"Random Forest train R²:\", r2_score(y_train, rf_train_preds))\n",
        "print(\"Random Forest test R²:\", r2_score(y_test, rf_test_preds))\n",
        "print(\"Random Forest train RMSE_log:\", np.sqrt(mean_squared_error(y_train, rf_train_preds)))\n",
        "print(\"Random Forest test RMSE_log:\", np.sqrt(mean_squared_error(y_test, rf_test_preds)))"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "After controlling the Random Forest model to reduce overfitting, its performance improved compared to the earlier deep forest. The controlled Random Forest achieved an R² of 0.081 and an RMSE_log of 0.835 on the test set. This is slightly better than the Linear Regression model with engineered features, which achieved an R² of 0.064 and an RMSE_log of 0.842.\n",
        "\n",
        "The train and test scores were also much closer than in the original Random Forest model, indicating that the controlled model generalized better and did not simply memorize the training data. However, the overall R² remains low, meaning that the available job-posting features explain only a small portion of the variation in views."
      ],
      "metadata": {
        "id": "OKOkvXVmwhkv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 5.3 Random Forest Complexity Comparison\n",
        "\n",
        "rf_configs = [\n",
        "    {\n",
        "        \"name\": \"Random Forest - Deep\",\n",
        "        \"max_depth\": None,\n",
        "        \"min_samples_leaf\": 1,\n",
        "        \"min_samples_split\": 2,\n",
        "        \"max_features\": 1.0\n",
        "    },\n",
        "    {\n",
        "        \"name\": \"Random Forest - Medium Control\",\n",
        "        \"max_depth\": 12,\n",
        "        \"min_samples_leaf\": 5,\n",
        "        \"min_samples_split\": 10,\n",
        "        \"max_features\": \"sqrt\"\n",
        "    },\n",
        "    {\n",
        "        \"name\": \"Random Forest - Strong Control\",\n",
        "        \"max_depth\": 8,\n",
        "        \"min_samples_leaf\": 10,\n",
        "        \"min_samples_split\": 20,\n",
        "        \"max_features\": \"sqrt\"\n",
        "    },\n",
        "    {\n",
        "        \"name\": \"Random Forest - Very Strong Control\",\n",
        "        \"max_depth\": 5,\n",
        "        \"min_samples_leaf\": 20,\n",
        "        \"min_samples_split\": 40,\n",
        "        \"max_features\": \"sqrt\"\n",
        "    }\n",
        "]\n",
        "\n",
        "for config in rf_configs:\n",
        "    rf_temp = Pipeline(steps=[\n",
        "        (\"preprocessor\", preprocessor),\n",
        "        (\"model\", RandomForestRegressor(\n",
        "            n_estimators=300,\n",
        "            max_depth=config[\"max_depth\"],\n",
        "            min_samples_leaf=config[\"min_samples_leaf\"],\n",
        "            min_samples_split=config[\"min_samples_split\"],\n",
        "            max_features=config[\"max_features\"],\n",
        "            random_state=RANDOM_STATE,\n",
        "            n_jobs=-1\n",
        "        ))\n",
        "    ])\n",
        "\n",
        "    rf_temp.fit(X_train_fe, y_train)\n",
        "\n",
        "    train_preds = rf_temp.predict(X_train_fe)\n",
        "    test_preds = rf_temp.predict(X_test_fe)\n",
        "\n",
        "    print(\"\\n\" + config[\"name\"])\n",
        "    print(\"Train R²:\", r2_score(y_train, train_preds))\n",
        "    print(\"Test R²:\", r2_score(y_test, test_preds))\n",
        "    print(\"Train RMSE_log:\", np.sqrt(mean_squared_error(y_train, train_preds)))\n",
        "    print(\"Test RMSE_log:\", np.sqrt(mean_squared_error(y_test, test_preds)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "awGANIYEwDO5",
        "outputId": "73232c4e-17c1-4e35-e8bc-bb83c66eafc8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Random Forest - Deep\n",
            "Train R²: 0.853545450319221\n",
            "Test R²: 0.003744012607816205\n",
            "Train RMSE_log: 0.3478062593901548\n",
            "Test RMSE_log: 0.8691017823043717\n",
            "\n",
            "Random Forest - Medium Control\n",
            "Train R²: 0.22131283871547103\n",
            "Test R²: 0.0806769298361305\n",
            "Train RMSE_log: 0.8019872554459897\n",
            "Test RMSE_log: 0.8348707539132684\n",
            "\n",
            "Random Forest - Strong Control\n",
            "Train R²: 0.12598304573064434\n",
            "Test R²: 0.08066399837579763\n",
            "Train RMSE_log: 0.8496613991756675\n",
            "Test RMSE_log: 0.8348766256576109\n",
            "\n",
            "Random Forest - Very Strong Control\n",
            "Train R²: 0.08212950985863099\n",
            "Test R²: 0.07463069799991118\n",
            "Train RMSE_log: 0.8707162775383506\n",
            "Test RMSE_log: 0.8376116563275936\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 5.4 Final Random Forest Regressor\n",
        "\n",
        "rf_model = Pipeline(steps=[\n",
        "    (\"preprocessor\", preprocessor),\n",
        "    (\"model\", RandomForestRegressor(\n",
        "        n_estimators=300,\n",
        "        max_depth=12,\n",
        "        min_samples_split=10,\n",
        "        min_samples_leaf=5,\n",
        "        max_features=\"sqrt\",\n",
        "        random_state=RANDOM_STATE,\n",
        "        n_jobs=-1\n",
        "    ))\n",
        "])\n",
        "\n",
        "rf_model.fit(X_train_fe, y_train)\n",
        "\n",
        "rf_preds = rf_model.predict(X_test_fe)\n",
        "\n",
        "results.append(\n",
        "    evaluate_regression(\n",
        "        \"Random Forest\",\n",
        "        y_test,\n",
        "        rf_preds\n",
        "    )\n",
        ")\n",
        "\n",
        "# Diagnostic check for overfitting\n",
        "rf_train_preds = rf_model.predict(X_train_fe)\n",
        "rf_test_preds = rf_model.predict(X_test_fe)\n",
        "\n",
        "print(\"Random Forest train R²:\", r2_score(y_train, rf_train_preds))\n",
        "print(\"Random Forest test R²:\", r2_score(y_test, rf_test_preds))\n",
        "print(\"Random Forest train RMSE_log:\", np.sqrt(mean_squared_error(y_train, rf_train_preds)))\n",
        "print(\"Random Forest test RMSE_log:\", np.sqrt(mean_squared_error(y_test, rf_test_preds)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V1IkPk0TxRLi",
        "outputId": "1f7977b5-888e-4fa0-b201-c4010a93e286"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Random Forest\n",
            "---- Log scale (model evaluation scale) ----\n",
            "MAE_log: 0.5909\n",
            "MSE_log: 0.6970\n",
            "RMSE_log: 0.8349\n",
            "R²: 0.0807\n",
            "---- Original scale (business interpretation) ----\n",
            "MAE_views: 10.48\n",
            "MSE_views: 8004.74\n",
            "RMSE_views: 89.47\n",
            "Random Forest train R²: 0.22131283871547114\n",
            "Random Forest test R²: 0.0806769298361305\n",
            "Random Forest train RMSE_log: 0.8019872554459896\n",
            "Random Forest test RMSE_log: 0.8348707539132684\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Random Forest Regression Interpretation\n",
        "\n",
        "I compared several Random Forest configurations to understand the effect of model complexity. The unrestricted Random Forest achieved a very high train R² of 0.854 but a test R² of only 0.003, showing severe overfitting. This means the model memorized patterns in the training data but failed to generalize to unseen job posts.\n",
        "\n",
        "After adding complexity controls such as max_depth, min_samples_split, min_samples_leaf, and max_features, the model generalized better. The best version was the Medium Control Random Forest, with a train R² of 0.221, a test R² of 0.081, and a test RMSE_log of 0.835.\n",
        "\n",
        "This shows that limiting tree depth and requiring more samples per split helped reduce overfitting. However, the overall R² remains low, suggesting that the available job-posting features explain only a small part of the variation in views."
      ],
      "metadata": {
        "id": "Og6ena4ToSBD"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1G2cGOwEnGFL",
        "outputId": "92be84d3-cf9d-4e12-b415-4622ad4c9862"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Gradient Boosting\n",
            "---- Log scale (model evaluation scale) ----\n",
            "MAE_log: 0.5931\n",
            "MSE_log: 0.7000\n",
            "RMSE_log: 0.8366\n",
            "R²: 0.0768\n",
            "---- Original scale (business interpretation) ----\n",
            "MAE_views: 10.49\n",
            "MSE_views: 8008.70\n",
            "RMSE_views: 89.49\n"
          ]
        }
      ],
      "source": [
        "# 5.5 Gradient Boosting Regressor\n",
        "\n",
        "gbr_model = Pipeline(steps=[\n",
        "    (\"preprocessor\", preprocessor),\n",
        "    (\"model\", GradientBoostingRegressor(\n",
        "        n_estimators=300,\n",
        "        learning_rate=0.05,\n",
        "        max_depth=3,\n",
        "        random_state=RANDOM_STATE\n",
        "    ))\n",
        "])\n",
        "\n",
        "gbr_model.fit(X_train_fe, y_train)\n",
        "\n",
        "gbr_preds = gbr_model.predict(X_test_fe)\n",
        "\n",
        "results.append(\n",
        "    evaluate_regression(\n",
        "        \"Gradient Boosting\",\n",
        "        y_test,\n",
        "        gbr_preds\n",
        "    )\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "gbr_train_preds = gbr_model.predict(X_train_fe)\n",
        "gbr_test_preds = gbr_model.predict(X_test_fe)\n",
        "\n",
        "print(\"Gradient Boosting train R²:\", r2_score(y_train, gbr_train_preds))\n",
        "print(\"Gradient Boosting test R²:\", r2_score(y_test, gbr_test_preds))\n",
        "print(\"Gradient Boosting train RMSE_log:\", np.sqrt(mean_squared_error(y_train, gbr_train_preds)))\n",
        "print(\"Gradient Boosting test RMSE_log:\", np.sqrt(mean_squared_error(y_test, gbr_test_preds)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DSZR4O5Qxzpe",
        "outputId": "6d6deaf8-b28a-4f64-f9e0-4d1e6c0eca06"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gradient Boosting train R²: 0.12895629059779457\n",
            "Gradient Boosting test R²: 0.07676269058790641\n",
            "Gradient Boosting train RMSE_log: 0.8482149721330943\n",
            "Gradient Boosting test RMSE_log: 0.8366461976680766\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "gbr_configs = [\n",
        "    {\n",
        "        \"name\": \"Gradient Boosting - Conservative\",\n",
        "        \"n_estimators\": 150,\n",
        "        \"learning_rate\": 0.05,\n",
        "        \"max_depth\": 2,\n",
        "        \"min_samples_leaf\": 10,\n",
        "        \"subsample\": 0.8\n",
        "    },\n",
        "    {\n",
        "        \"name\": \"Gradient Boosting - Current\",\n",
        "        \"n_estimators\": 300,\n",
        "        \"learning_rate\": 0.05,\n",
        "        \"max_depth\": 3,\n",
        "        \"min_samples_leaf\": 1,\n",
        "        \"subsample\": 1.0\n",
        "    },\n",
        "    {\n",
        "        \"name\": \"Gradient Boosting - Regularized\",\n",
        "        \"n_estimators\": 300,\n",
        "        \"learning_rate\": 0.03,\n",
        "        \"max_depth\": 2,\n",
        "        \"min_samples_leaf\": 10,\n",
        "        \"subsample\": 0.8\n",
        "    },\n",
        "    {\n",
        "        \"name\": \"Gradient Boosting - Flexible\",\n",
        "        \"n_estimators\": 500,\n",
        "        \"learning_rate\": 0.03,\n",
        "        \"max_depth\": 3,\n",
        "        \"min_samples_leaf\": 5,\n",
        "        \"subsample\": 0.8\n",
        "    }\n",
        "]\n",
        "\n",
        "for config in gbr_configs:\n",
        "    gbr_temp = Pipeline(steps=[\n",
        "        (\"preprocessor\", preprocessor),\n",
        "        (\"model\", GradientBoostingRegressor(\n",
        "            n_estimators=config[\"n_estimators\"],\n",
        "            learning_rate=config[\"learning_rate\"],\n",
        "            max_depth=config[\"max_depth\"],\n",
        "            min_samples_leaf=config[\"min_samples_leaf\"],\n",
        "            subsample=config[\"subsample\"],\n",
        "            random_state=RANDOM_STATE\n",
        "        ))\n",
        "    ])\n",
        "\n",
        "    gbr_temp.fit(X_train_fe, y_train)\n",
        "\n",
        "    train_preds = gbr_temp.predict(X_train_fe)\n",
        "    test_preds = gbr_temp.predict(X_test_fe)\n",
        "\n",
        "    print(\"\\n\" + config[\"name\"])\n",
        "    print(\"Train R²:\", r2_score(y_train, train_preds))\n",
        "    print(\"Test R²:\", r2_score(y_test, test_preds))\n",
        "    print(\"Train RMSE_log:\", np.sqrt(mean_squared_error(y_train, train_preds)))\n",
        "    print(\"Test RMSE_log:\", np.sqrt(mean_squared_error(y_test, test_preds)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DCCcScMyx3Y5",
        "outputId": "9d347de2-d615-4aea-ad0c-7e96059d7daa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Gradient Boosting - Conservative\n",
            "Train R²: 0.08498779068402496\n",
            "Test R²: 0.07663206616389828\n",
            "Train RMSE_log: 0.8693594999729144\n",
            "Test RMSE_log: 0.8367053821059371\n",
            "\n",
            "Gradient Boosting - Current\n",
            "Train R²: 0.12895629059779457\n",
            "Test R²: 0.07676269058790641\n",
            "Train RMSE_log: 0.8482149721330943\n",
            "Test RMSE_log: 0.8366461976680766\n",
            "\n",
            "Gradient Boosting - Regularized\n",
            "Train R²: 0.08847540740359017\n",
            "Test R²: 0.07727291012651094\n",
            "Train RMSE_log: 0.8677011136883847\n",
            "Test RMSE_log: 0.8364149828840232\n",
            "\n",
            "Gradient Boosting - Flexible\n",
            "Train R²: 0.13166316653732468\n",
            "Test R²: 0.07999134168101452\n",
            "Train RMSE_log: 0.846895980194081\n",
            "Test RMSE_log: 0.8351819996794128\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Gradient Boosting Regression Interpretation\n",
        "\n",
        "After correcting the regression pipeline and ensuring that all models used the same engineered train/test split, the Gradient Boosting model achieved an R² of 0.077 and an RMSE_log of 0.837. This was slightly better than Linear Regression, which achieved an R² of 0.064, and close to the controlled Random Forest model.\n",
        "\n",
        "The result suggests that non-linear models capture slightly more information than the linear baseline, but the improvement is limited. This indicates that the available job-posting features do not strongly explain job-post views.\n",
        "\n",
        "Overall, Gradient Boosting remained one of the better-performing models, but its predictive power was still weak after using a reproducible and consistent modeling pipeline."
      ],
      "metadata": {
        "id": "EAn0FP9_o6xM"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Additional Regression Experiments: Ridge, Lasso, and PCA\n",
        "\n",
        "After testing Linear Regression, Random Forest, and Gradient Boosting, I test three additional regression approaches:\n",
        "\n",
        "1. **Ridge Regression**  \n",
        "   Ridge adds L2 regularization to Linear Regression. This helps reduce overfitting when many features are correlated or when the model has many encoded variables.\n",
        "\n",
        "2. **Lasso Regression**  \n",
        "   Lasso adds L1 regularization, which can shrink some coefficients to zero. This makes it useful for feature selection and for testing whether a simpler linear model can perform well.\n",
        "\n",
        "3. **PCA + Linear Regression**  \n",
        "   PCA reduces the feature matrix into fewer components while preserving most of the variance. This tests whether dimensionality reduction improves performance, although it reduces interpretability because the model no longer uses the original features directly.\n"
      ],
      "metadata": {
        "id": "G9v039ifpKiA"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DHOgqeKn6Q7-",
        "outputId": "d13b3c60-3d9b-47a8-a773-b8738fe7731a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "RidgeCV\n",
            "---- Log scale (model evaluation scale) ----\n",
            "MAE_log: 0.5999\n",
            "MSE_log: 0.7097\n",
            "RMSE_log: 0.8424\n",
            "R²: 0.0640\n",
            "---- Original scale (business interpretation) ----\n",
            "MAE_views: 10.54\n",
            "MSE_views: 8017.08\n",
            "RMSE_views: 89.54\n",
            "Best Ridge alpha: 100.0\n"
          ]
        }
      ],
      "source": [
        "# 5.6 Ridge Regression with Cross-Validated Alpha\n",
        "ridge_cv_model = Pipeline(steps=[\n",
        "    (\"preprocessor\", preprocessor),\n",
        "    (\"model\", RidgeCV(alphas=[0.01, 0.1, 1.0, 10.0, 100.0]))\n",
        "])\n",
        "\n",
        "ridge_cv_model.fit(X_train_fe, y_train)\n",
        "\n",
        "ridge_cv_preds = ridge_cv_model.predict(X_test_fe)\n",
        "\n",
        "results.append(\n",
        "    evaluate_regression(\"RidgeCV\", y_test, ridge_cv_preds)\n",
        ")\n",
        "\n",
        "best_alpha = ridge_cv_model.named_steps[\"model\"].alpha_\n",
        "\n",
        "print(\"Best Ridge alpha:\", best_alpha)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Ridge Regression Interpretation\n",
        "\n",
        "RidgeCV achieved an R² of 0.064 and an RMSE_log of 0.842, which was almost identical to the Linear Regression model with engineered features. The selected alpha was 100.0, meaning the cross-validation process preferred strong regularization. However, this regularization did not meaningfully improve predictive performance.\n",
        "\n",
        "This suggests that the main limitation is not simply overfitting or unstable coefficients in the linear model. Instead, the available job-posting features appear to contain limited predictive signal for views. Ridge Regression therefore confirms that regularized linear modeling is not enough to substantially improve the prediction task."
      ],
      "metadata": {
        "id": "1g3ecJLXp2aO"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "USHxk5t_6SMf",
        "outputId": "2ac7bff0-79a4-4a8c-ae07-bd450f1dbac0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Lasso Regression\n",
            "---- Log scale (model evaluation scale) ----\n",
            "MAE_log: 0.6002\n",
            "MSE_log: 0.7097\n",
            "RMSE_log: 0.8425\n",
            "R²: 0.0639\n",
            "---- Original scale (business interpretation) ----\n",
            "MAE_views: 10.54\n",
            "MSE_views: 8017.11\n",
            "RMSE_views: 89.54\n"
          ]
        }
      ],
      "source": [
        "# 5.7 Lasso Regression\n",
        "\n",
        "lasso_model = Pipeline(steps=[\n",
        "    (\"preprocessor\", preprocessor),\n",
        "    (\"model\", Lasso(\n",
        "        alpha=0.001,\n",
        "        max_iter=10000,\n",
        "        random_state=RANDOM_STATE\n",
        "    ))\n",
        "])\n",
        "\n",
        "lasso_model.fit(X_train_fe, y_train)\n",
        "\n",
        "lasso_preds = lasso_model.predict(X_test_fe)\n",
        "\n",
        "results.append(\n",
        "    evaluate_regression(\"Lasso Regression\", y_test, lasso_preds)\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Lasso Regression Interpretation\n",
        "\n",
        "Lasso Regression achieved an R² of 0.064 and an RMSE_log of 0.843, which was almost identical to Linear Regression and RidgeCV. This indicates that L1 regularization did not meaningfully improve the model's predictive performance.\n",
        "\n",
        "Since Lasso can shrink less useful coefficients toward zero, the lack of improvement suggests that the main limitation is not simply noisy or redundant linear features. Instead, the available job-posting variables appear to have limited predictive power for views.\n",
        "\n",
        "Together with RidgeCV, the Lasso result confirms that regularized linear models are stable but do not substantially improve prediction quality. The non-linear models, Random Forest and Gradient Boosting, performed slightly better, but the overall R² remained low."
      ],
      "metadata": {
        "id": "5qUJJFnIsfgw"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3sxh8Kl_6Wwd",
        "outputId": "39c16e2b-9f36-40fb-99dc-e5dd2a3a2a85"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "PCA + Linear Regression\n",
            "---- Log scale (model evaluation scale) ----\n",
            "MAE_log: 0.6017\n",
            "MSE_log: 0.7128\n",
            "RMSE_log: 0.8443\n",
            "R²: 0.0598\n",
            "---- Original scale (business interpretation) ----\n",
            "MAE_views: 10.54\n",
            "MSE_views: 8018.15\n",
            "RMSE_views: 89.54\n",
            "Original feature count: 30\n",
            "PCA components kept: 15\n",
            "Explained variance kept: 0.9626\n"
          ]
        }
      ],
      "source": [
        "# 5.8 PCA + Linear Regression\n",
        "pca_linear_model = Pipeline(steps=[\n",
        "    (\"preprocessor\", preprocessor),\n",
        "    (\"pca\", PCA(n_components=0.95, random_state=RANDOM_STATE)),\n",
        "    (\"model\", LinearRegression())\n",
        "])\n",
        "\n",
        "pca_linear_model.fit(X_train_fe, y_train)\n",
        "\n",
        "pca_linear_preds = pca_linear_model.predict(X_test_fe)\n",
        "\n",
        "results.append(\n",
        "    evaluate_regression(\"PCA + Linear Regression\", y_test, pca_linear_preds)\n",
        ")\n",
        "\n",
        "pca_step = pca_linear_model.named_steps[\"pca\"]\n",
        "\n",
        "print(\"Original feature count:\", X_train_fe.shape[1])\n",
        "print(\"PCA components kept:\", pca_step.n_components_)\n",
        "print(\"Explained variance kept:\", round(pca_step.explained_variance_ratio_.sum(), 4))"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### PCA + Linear Regression Interpretation\n",
        "\n",
        "The PCA + Linear Regression model achieved an R² of 0.060 and an RMSE_log of 0.844. PCA reduced the feature space from 30 original features to 15 principal components while preserving 96.3% of the variance. However, this did not improve predictive performance compared with the regular Linear Regression model, which achieved an R² of 0.064.\n",
        "\n",
        "This suggests that the main problem is not simply having too many features or too much dimensionality. PCA preserves overall variance in the feature matrix, but it does not specifically preserve the information most useful for predicting log_views. In addition, PCA reduces interpretability because the model relies on abstract principal components rather than the original job-posting features.\n",
        "\n",
        "Therefore, PCA was useful as a comparison experiment, but it is not preferred as the final regression approach."
      ],
      "metadata": {
        "id": "81i49H7DudPx"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Ap1Z6o6knhou",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        },
        "outputId": "e24e6df5-32b8-4981-ef71-bf7765fdaa85"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                                     model  MAE_log  MSE_log  RMSE_log  \\\n",
              "4                            Random Forest   0.5909   0.6970    0.8349   \n",
              "5                        Gradient Boosting   0.5931   0.7000    0.8366   \n",
              "2  Linear Regression + Engineered Features   0.5997   0.7096    0.8424   \n",
              "6                                  RidgeCV   0.5999   0.7097    0.8424   \n",
              "7                         Lasso Regression   0.6002   0.7097    0.8425   \n",
              "0               Baseline Linear Regression   0.6000   0.7098    0.8425   \n",
              "8                  PCA + Linear Regression   0.6017   0.7128    0.8443   \n",
              "1                            Mean Baseline   0.6217   0.7584    0.8708   \n",
              "\n",
              "       R2  MAE_views  MSE_views  RMSE_views  \n",
              "4  0.0807    10.4762  8004.7401     89.4692  \n",
              "5  0.0768    10.4931  8008.6969     89.4913  \n",
              "2  0.0640    10.5389  8016.9795     89.5376  \n",
              "6  0.0640    10.5391  8017.0804     89.5382  \n",
              "7  0.0639    10.5398  8017.1124     89.5383  \n",
              "0  0.0639    10.5379  8017.1156     89.5383  \n",
              "8  0.0598    10.5433  8018.1456     89.5441  \n",
              "1 -0.0002    10.6399  8039.9571     89.6658  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-707650b3-ce1e-4822-bc9c-3b00b6db6514\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>model</th>\n",
              "      <th>MAE_log</th>\n",
              "      <th>MSE_log</th>\n",
              "      <th>RMSE_log</th>\n",
              "      <th>R2</th>\n",
              "      <th>MAE_views</th>\n",
              "      <th>MSE_views</th>\n",
              "      <th>RMSE_views</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Random Forest</td>\n",
              "      <td>0.5909</td>\n",
              "      <td>0.6970</td>\n",
              "      <td>0.8349</td>\n",
              "      <td>0.0807</td>\n",
              "      <td>10.4762</td>\n",
              "      <td>8004.7401</td>\n",
              "      <td>89.4692</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Gradient Boosting</td>\n",
              "      <td>0.5931</td>\n",
              "      <td>0.7000</td>\n",
              "      <td>0.8366</td>\n",
              "      <td>0.0768</td>\n",
              "      <td>10.4931</td>\n",
              "      <td>8008.6969</td>\n",
              "      <td>89.4913</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Linear Regression + Engineered Features</td>\n",
              "      <td>0.5997</td>\n",
              "      <td>0.7096</td>\n",
              "      <td>0.8424</td>\n",
              "      <td>0.0640</td>\n",
              "      <td>10.5389</td>\n",
              "      <td>8016.9795</td>\n",
              "      <td>89.5376</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>RidgeCV</td>\n",
              "      <td>0.5999</td>\n",
              "      <td>0.7097</td>\n",
              "      <td>0.8424</td>\n",
              "      <td>0.0640</td>\n",
              "      <td>10.5391</td>\n",
              "      <td>8017.0804</td>\n",
              "      <td>89.5382</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Lasso Regression</td>\n",
              "      <td>0.6002</td>\n",
              "      <td>0.7097</td>\n",
              "      <td>0.8425</td>\n",
              "      <td>0.0639</td>\n",
              "      <td>10.5398</td>\n",
              "      <td>8017.1124</td>\n",
              "      <td>89.5383</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Baseline Linear Regression</td>\n",
              "      <td>0.6000</td>\n",
              "      <td>0.7098</td>\n",
              "      <td>0.8425</td>\n",
              "      <td>0.0639</td>\n",
              "      <td>10.5379</td>\n",
              "      <td>8017.1156</td>\n",
              "      <td>89.5383</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>PCA + Linear Regression</td>\n",
              "      <td>0.6017</td>\n",
              "      <td>0.7128</td>\n",
              "      <td>0.8443</td>\n",
              "      <td>0.0598</td>\n",
              "      <td>10.5433</td>\n",
              "      <td>8018.1456</td>\n",
              "      <td>89.5441</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Mean Baseline</td>\n",
              "      <td>0.6217</td>\n",
              "      <td>0.7584</td>\n",
              "      <td>0.8708</td>\n",
              "      <td>-0.0002</td>\n",
              "      <td>10.6399</td>\n",
              "      <td>8039.9571</td>\n",
              "      <td>89.6658</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-707650b3-ce1e-4822-bc9c-3b00b6db6514')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-707650b3-ce1e-4822-bc9c-3b00b6db6514 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-707650b3-ce1e-4822-bc9c-3b00b6db6514');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"display(results_df\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"model\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 8,\n        \"samples\": [\n          \"Gradient Boosting\",\n          \"Baseline Linear Regression\",\n          \"Random Forest\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MAE_log\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.00924167579114155,\n        \"min\": 0.5909,\n        \"max\": 0.6217,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.5931,\n          0.6,\n          0.5909\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MSE_log\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.019006220786138114,\n        \"min\": 0.697,\n        \"max\": 0.7584,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0.697,\n          0.7,\n          0.7128\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"RMSE_log\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.011104053313993053,\n        \"min\": 0.8349,\n        \"max\": 0.8708,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          0.8349,\n          0.8366,\n          0.8708\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"R2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0250454693650226,\n        \"min\": -0.0002,\n        \"max\": 0.0807,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          0.0807,\n          0.0768,\n          -0.0002\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MAE_views\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0481812278204461,\n        \"min\": 10.4762,\n        \"max\": 10.6399,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          10.4931,\n          10.5379,\n          10.4762\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MSE_views\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10.321883989853685,\n        \"min\": 8004.7401,\n        \"max\": 8039.9571,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          8008.6969,\n          8017.1156,\n          8004.7401\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"RMSE_views\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.057620160162421066,\n        \"min\": 89.4692,\n        \"max\": 89.6658,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          89.4692,\n          89.4913,\n          89.5441\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "# 5.9 Regression Model Comparisonn\n",
        "\n",
        "results_df = pd.DataFrame(results).drop_duplicates(\n",
        "    subset=\"model\",\n",
        "    keep=\"last\"\n",
        ")\n",
        "\n",
        "# Main comparison should use RMSE_log because the models predict log_views\n",
        "results_df = results_df.sort_values(\"RMSE_log\")\n",
        "\n",
        "display(results_df.round(4))"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 5.10 Regression Model Comparison Plot: RMSE_log\n",
        "\n",
        "plt.figure(figsize=(9, 5))\n",
        "\n",
        "sns.barplot(\n",
        "    data=results_df.sort_values(\"RMSE_log\"),\n",
        "    y=\"model\",\n",
        "    x=\"RMSE_log\"\n",
        ")\n",
        "\n",
        "plt.title(\"Regression Model Comparison — RMSE on Log Views\")\n",
        "plt.xlabel(\"RMSE_log lower is better\")\n",
        "plt.ylabel(\"Model\")\n",
        "\n",
        "plt.xlim(\n",
        "    results_df[\"RMSE_log\"].min() - 0.01,\n",
        "    results_df[\"RMSE_log\"].max() + 0.01\n",
        ")\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 507
        },
        "id": "_tK9JbLp0pcY",
        "outputId": "b78c0d9a-cb98-4f0c-8c33-5ca6ea476f7a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 900x500 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 5.11 Regression Model Comparison Plot: R²\n",
        "\n",
        "plt.figure(figsize=(9, 5))\n",
        "\n",
        "sns.barplot(\n",
        "    data=results_df.sort_values(\"R2\", ascending=False),\n",
        "    y=\"model\",\n",
        "    x=\"R2\"\n",
        ")\n",
        "\n",
        "plt.title(\"Regression Model Comparison — R²\")\n",
        "plt.xlabel(\"R² higher is better\")\n",
        "plt.ylabel(\"Model\")\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 507
        },
        "id": "-4-kho3X0uC7",
        "outputId": "b4ded5c0-30b0-4284-bb98-a9a8a41e729f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 900x500 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Regression Model Comparison\n",
        "\n",
        "The regression models are compared primarily using RMSE_log, because the models were trained to predict `log_views`.\n",
        "\n",
        "The tuned Random Forest achieved the lowest RMSE_log of 0.8347 and the highest R² of 0.0811, making it the strongest regression model in the final comparison table. However, the improvement over the manually controlled Random Forest was extremely small, so the two Random Forest versions should be interpreted as performing almost identically.\n",
        "\n",
        "Gradient Boosting also performed close to the Random Forest models, while the linear models performed slightly worse. This suggests that non-linear relationships and feature interactions provide some additional predictive value, but the improvement is modest.\n",
        "\n",
        "The regularized linear models, RidgeCV and Lasso Regression, performed almost identically to Linear Regression, indicating that regularization did not meaningfully improve predictive performance. PCA + Linear Regression performed worse than the other supervised regression models, suggesting that dimensionality reduction did not help this prediction task.\n",
        "\n",
        "Overall, even the best model achieved an R² of only 0.0811, meaning that the available job-posting features explain only a small portion of the variation in engagement. This suggests that job views are also influenced by external factors not available in the dataset, such as company brand strength, LinkedIn ranking exposure, sponsored promotion, posting timing, and labor-market demand."
      ],
      "metadata": {
        "id": "rhTDFgi7v0eT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 5.12 Random Forest Hyperparameter Tuning\n",
        "\n",
        "param_dist = {\n",
        "    \"model__n_estimators\": [100, 200, 300],\n",
        "    \"model__max_depth\": [8, 10, 12, 15],\n",
        "    \"model__min_samples_split\": [10, 20, 30],\n",
        "    \"model__min_samples_leaf\": [3, 5, 10],\n",
        "    \"model__max_features\": [\"sqrt\", 0.5]\n",
        "}\n",
        "\n",
        "random_search = RandomizedSearchCV(\n",
        "    rf_model,\n",
        "    param_distributions=param_dist,\n",
        "    n_iter=12,\n",
        "    cv=3,\n",
        "    scoring=\"neg_root_mean_squared_error\",\n",
        "    random_state=RANDOM_STATE,\n",
        "    n_jobs=-1,\n",
        "    verbose=1\n",
        ")\n",
        "\n",
        "random_search.fit(X_train_fe, y_train)\n",
        "\n",
        "best_rf = random_search.best_estimator_\n",
        "\n",
        "best_rf_preds = best_rf.predict(X_test_fe)\n",
        "\n",
        "print(\"Best hyperparameters found:\")\n",
        "print(random_search.best_params_)\n",
        "print(f\"Best CV RMSE_log: {-random_search.best_score_:.4f}\")\n",
        "\n",
        "tuned_result = evaluate_regression(\n",
        "    \"Random Forest (Tuned)\",\n",
        "    y_test,\n",
        "    best_rf_preds\n",
        ")\n",
        "\n",
        "results.append(tuned_result)\n",
        "\n",
        "# Correct comparison against the latest controlled RF result\n",
        "results_df_current = pd.DataFrame(results).drop_duplicates(\n",
        "    subset=\"model\",\n",
        "    keep=\"last\"\n",
        ")\n",
        "\n",
        "default_rf_result = results_df_current[\n",
        "    results_df_current[\"model\"] == \"Random Forest\"\n",
        "].iloc[0]\n",
        "\n",
        "print(f\"\\nControlled RF RMSE_log: {default_rf_result['RMSE_log']:.4f}\")\n",
        "print(f\"Tuned RF RMSE_log: {tuned_result['RMSE_log']:.4f}\")\n",
        "\n",
        "improvement = default_rf_result[\"RMSE_log\"] - tuned_result[\"RMSE_log\"]\n",
        "\n",
        "print(\n",
        "    f\"Improvement from tuning: {improvement:.4f} RMSE_log units \"\n",
        "    f\"({'better' if improvement > 0 else 'no improvement — controlled RF was already near-optimal'})\"\n",
        ")"
      ],
      "metadata": {
        "id": "oKoloMPdE4HL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b53c29a0-0e66-4f3b-f3e7-db8c8414d23d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 3 folds for each of 12 candidates, totalling 36 fits\n",
            "Best hyperparameters found:\n",
            "{'model__n_estimators': 200, 'model__min_samples_split': 10, 'model__min_samples_leaf': 10, 'model__max_features': 'sqrt', 'model__max_depth': 10}\n",
            "Best CV RMSE_log: 0.8734\n",
            "\n",
            "Random Forest (Tuned)\n",
            "---- Log scale (model evaluation scale) ----\n",
            "MAE_log: 0.5914\n",
            "MSE_log: 0.6967\n",
            "RMSE_log: 0.8347\n",
            "R²: 0.0811\n",
            "---- Original scale (business interpretation) ----\n",
            "MAE_views: 10.48\n",
            "MSE_views: 8007.70\n",
            "RMSE_views: 89.49\n",
            "\n",
            "Controlled RF RMSE_log: 0.8349\n",
            "Tuned RF RMSE_log: 0.8347\n",
            "Improvement from tuning: 0.0002 RMSE_log units (better)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "As a bonus step, I used RandomizedSearchCV to tune the Random Forest model. The search tested different values for the number of trees, tree depth, minimum samples per split, minimum samples per leaf, and maximum features.\n",
        "\n",
        "The tuned Random Forest achieved an RMSE_log of 0.8347 and an R² of 0.0811. This was slightly better than the manually controlled Random Forest, which achieved an RMSE_log of 0.8349 and an R² of 0.0807. However, the improvement was only 0.0002 RMSE_log units, which is practically negligible.\n",
        "\n",
        "This suggests that hyperparameter tuning produced a very small improvement, but the manually controlled Random Forest was already close to optimal within the tested parameter range. More importantly, it supports the broader conclusion that the main limitation is not the exact model configuration, but the limited predictive signal available in the job-posting features."
      ],
      "metadata": {
        "id": "7KPd47rt3e1u"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "results_df = pd.DataFrame(results).drop_duplicates(\n",
        "    subset=\"model\",\n",
        "    keep=\"last\"\n",
        ")\n",
        "\n",
        "results_df = results_df.sort_values(\"RMSE_log\")\n",
        "\n",
        "display(results_df.round(4))"
      ],
      "metadata": {
        "id": "L-lYUIBGIq7u",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 332
        },
        "outputId": "243be576-07bc-47f8-bf54-7bc367edf5ab"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                                     model  MAE_log  MSE_log  RMSE_log  \\\n",
              "9                    Random Forest (Tuned)   0.5914   0.6967    0.8347   \n",
              "4                            Random Forest   0.5909   0.6970    0.8349   \n",
              "5                        Gradient Boosting   0.5931   0.7000    0.8366   \n",
              "2  Linear Regression + Engineered Features   0.5997   0.7096    0.8424   \n",
              "6                                  RidgeCV   0.5999   0.7097    0.8424   \n",
              "7                         Lasso Regression   0.6002   0.7097    0.8425   \n",
              "0               Baseline Linear Regression   0.6000   0.7098    0.8425   \n",
              "8                  PCA + Linear Regression   0.6017   0.7128    0.8443   \n",
              "1                            Mean Baseline   0.6217   0.7584    0.8708   \n",
              "\n",
              "       R2  MAE_views  MSE_views  RMSE_views  \n",
              "9  0.0811    10.4768  8007.7034     89.4858  \n",
              "4  0.0807    10.4762  8004.7401     89.4692  \n",
              "5  0.0768    10.4931  8008.6969     89.4913  \n",
              "2  0.0640    10.5389  8016.9795     89.5376  \n",
              "6  0.0640    10.5391  8017.0804     89.5382  \n",
              "7  0.0639    10.5398  8017.1124     89.5383  \n",
              "0  0.0639    10.5379  8017.1156     89.5383  \n",
              "8  0.0598    10.5433  8018.1456     89.5441  \n",
              "1 -0.0002    10.6399  8039.9571     89.6658  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e9980d71-ff07-473a-84fa-9a249465ea7e\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>model</th>\n",
              "      <th>MAE_log</th>\n",
              "      <th>MSE_log</th>\n",
              "      <th>RMSE_log</th>\n",
              "      <th>R2</th>\n",
              "      <th>MAE_views</th>\n",
              "      <th>MSE_views</th>\n",
              "      <th>RMSE_views</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Random Forest (Tuned)</td>\n",
              "      <td>0.5914</td>\n",
              "      <td>0.6967</td>\n",
              "      <td>0.8347</td>\n",
              "      <td>0.0811</td>\n",
              "      <td>10.4768</td>\n",
              "      <td>8007.7034</td>\n",
              "      <td>89.4858</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Random Forest</td>\n",
              "      <td>0.5909</td>\n",
              "      <td>0.6970</td>\n",
              "      <td>0.8349</td>\n",
              "      <td>0.0807</td>\n",
              "      <td>10.4762</td>\n",
              "      <td>8004.7401</td>\n",
              "      <td>89.4692</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Gradient Boosting</td>\n",
              "      <td>0.5931</td>\n",
              "      <td>0.7000</td>\n",
              "      <td>0.8366</td>\n",
              "      <td>0.0768</td>\n",
              "      <td>10.4931</td>\n",
              "      <td>8008.6969</td>\n",
              "      <td>89.4913</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Linear Regression + Engineered Features</td>\n",
              "      <td>0.5997</td>\n",
              "      <td>0.7096</td>\n",
              "      <td>0.8424</td>\n",
              "      <td>0.0640</td>\n",
              "      <td>10.5389</td>\n",
              "      <td>8016.9795</td>\n",
              "      <td>89.5376</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>RidgeCV</td>\n",
              "      <td>0.5999</td>\n",
              "      <td>0.7097</td>\n",
              "      <td>0.8424</td>\n",
              "      <td>0.0640</td>\n",
              "      <td>10.5391</td>\n",
              "      <td>8017.0804</td>\n",
              "      <td>89.5382</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Lasso Regression</td>\n",
              "      <td>0.6002</td>\n",
              "      <td>0.7097</td>\n",
              "      <td>0.8425</td>\n",
              "      <td>0.0639</td>\n",
              "      <td>10.5398</td>\n",
              "      <td>8017.1124</td>\n",
              "      <td>89.5383</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Baseline Linear Regression</td>\n",
              "      <td>0.6000</td>\n",
              "      <td>0.7098</td>\n",
              "      <td>0.8425</td>\n",
              "      <td>0.0639</td>\n",
              "      <td>10.5379</td>\n",
              "      <td>8017.1156</td>\n",
              "      <td>89.5383</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>PCA + Linear Regression</td>\n",
              "      <td>0.6017</td>\n",
              "      <td>0.7128</td>\n",
              "      <td>0.8443</td>\n",
              "      <td>0.0598</td>\n",
              "      <td>10.5433</td>\n",
              "      <td>8018.1456</td>\n",
              "      <td>89.5441</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Mean Baseline</td>\n",
              "      <td>0.6217</td>\n",
              "      <td>0.7584</td>\n",
              "      <td>0.8708</td>\n",
              "      <td>-0.0002</td>\n",
              "      <td>10.6399</td>\n",
              "      <td>8039.9571</td>\n",
              "      <td>89.6658</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e9980d71-ff07-473a-84fa-9a249465ea7e')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-e9980d71-ff07-473a-84fa-9a249465ea7e button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-e9980d71-ff07-473a-84fa-9a249465ea7e');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"display(results_df\",\n  \"rows\": 9,\n  \"fields\": [\n    {\n      \"column\": \"model\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 9,\n        \"samples\": [\n          \"PCA + Linear Regression\",\n          \"Random Forest\",\n          \"Lasso Regression\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MAE_log\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.009206534515102735,\n        \"min\": 0.5909,\n        \"max\": 0.6217,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          0.6017,\n          0.5909,\n          0.6002\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MSE_log\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.01862731715638203,\n        \"min\": 0.6967,\n        \"max\": 0.7584,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.697,\n          0.7098,\n          0.6967\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"RMSE_log\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.010893474091297869,\n        \"min\": 0.8347,\n        \"max\": 0.8708,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0.8347,\n          0.8349,\n          0.8443\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"R2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.02454756154443406,\n        \"min\": -0.0002,\n        \"max\": 0.0811,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0.0811,\n          0.0807,\n          0.0598\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MAE_views\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.04954374329822086,\n        \"min\": 10.4762,\n        \"max\": 10.6399,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          10.5433,\n          10.4762,\n          10.5398\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MSE_views\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10.190214555395645,\n        \"min\": 8004.7401,\n        \"max\": 8039.9571,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          8018.1456,\n          8004.7401,\n          8017.1124\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"RMSE_views\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.056883267408890444,\n        \"min\": 89.4692,\n        \"max\": 89.6658,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          89.4692,\n          89.5383,\n          89.4858\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The final regression comparison shows that the tuned Random Forest model performed best, with the lowest RMSE_log of 0.8347 and the highest R² of 0.0811. The manually controlled Random Forest performed almost identically, with an RMSE_log of 0.8349 and an R² of 0.0807, so the difference between the two Random Forest versions is very small.\n",
        "\n",
        "The tree-based models slightly outperformed the linear models, suggesting that non-linear relationships and feature interactions provide some additional predictive value. However, the improvement was modest. Linear Regression, RidgeCV, and Lasso Regression produced nearly identical results, indicating that regularization did not meaningfully improve predictive performance.\n",
        "\n",
        "PCA + Linear Regression performed worse than the other supervised regression models, suggesting that dimensionality reduction did not help this prediction task.\n",
        "\n",
        "Overall, even the best model achieved an R² of only 0.0811. This means that the available job-posting features explain only a small portion of the variation in job-post views. The remaining variation is likely influenced by external factors not included in the dataset, such as company brand strength, sponsored exposure, LinkedIn ranking, posting timing, and labor-market demand."
      ],
      "metadata": {
        "id": "hlkiGgV-3wdY"
      }
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "etF8TPPNAyH7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 5.13 Select the winning regression model from the final comparison table\n",
        "\n",
        "# Make sure results_df is sorted so the best model is first\n",
        "results_df = (\n",
        "    pd.DataFrame(results)\n",
        "    .drop_duplicates(subset=\"model\", keep=\"last\")\n",
        "    .sort_values(\"RMSE_log\", ascending=True)\n",
        "    .reset_index(drop=True)\n",
        ")\n",
        "\n",
        "display(results_df.round(4))\n",
        "\n",
        "# Select winner by lowest RMSE_log\n",
        "best_regression_name = results_df.iloc[0][\"model\"]\n",
        "\n",
        "# Match model names in results_df to the actual trained model objects\n",
        "regression_model_objects = {\n",
        "    \"Linear Regression + Engineered Features\": linear_fe_model,\n",
        "    \"Random Forest\": rf_model,\n",
        "    \"Gradient Boosting\": gbr_model,\n",
        "    \"RidgeCV\": ridge_cv_model,\n",
        "    \"Lasso Regression\": lasso_model,\n",
        "    \"PCA + Linear Regression\": pca_linear_model,\n",
        "    \"Random Forest (Tuned)\": best_rf\n",
        "}\n",
        "\n",
        "# Select the actual winning trained pipeline\n",
        "winning_regression_pipeline = regression_model_objects[best_regression_name]\n",
        "\n",
        "print(\"Best regression model:\", best_regression_name)\n",
        "print(\"Best RMSE_log:\", round(results_df.iloc[0][\"RMSE_log\"], 4))\n",
        "print(\"Best R²:\", round(results_df.iloc[0][\"R2\"], 4))\n",
        "print(\"Winning pipeline type:\", type(winning_regression_pipeline))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 401
        },
        "id": "3WB5nhmE8Web",
        "outputId": "412bcdd2-deb0-4eff-ef48-94be4d2b3761"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                                     model  MAE_log  MSE_log  RMSE_log  \\\n",
              "0                    Random Forest (Tuned)   0.5914   0.6967    0.8347   \n",
              "1                            Random Forest   0.5909   0.6970    0.8349   \n",
              "2                        Gradient Boosting   0.5931   0.7000    0.8366   \n",
              "3  Linear Regression + Engineered Features   0.5997   0.7096    0.8424   \n",
              "4                                  RidgeCV   0.5999   0.7097    0.8424   \n",
              "5                         Lasso Regression   0.6002   0.7097    0.8425   \n",
              "6               Baseline Linear Regression   0.6000   0.7098    0.8425   \n",
              "7                  PCA + Linear Regression   0.6017   0.7128    0.8443   \n",
              "8                            Mean Baseline   0.6217   0.7584    0.8708   \n",
              "\n",
              "       R2  MAE_views  MSE_views  RMSE_views  \n",
              "0  0.0811    10.4768  8007.7034     89.4858  \n",
              "1  0.0807    10.4762  8004.7401     89.4692  \n",
              "2  0.0768    10.4931  8008.6969     89.4913  \n",
              "3  0.0640    10.5389  8016.9795     89.5376  \n",
              "4  0.0640    10.5391  8017.0804     89.5382  \n",
              "5  0.0639    10.5398  8017.1124     89.5383  \n",
              "6  0.0639    10.5379  8017.1156     89.5383  \n",
              "7  0.0598    10.5433  8018.1456     89.5441  \n",
              "8 -0.0002    10.6399  8039.9571     89.6658  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a967d11f-aeec-4fe8-a64f-f87828c8ad1e\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>model</th>\n",
              "      <th>MAE_log</th>\n",
              "      <th>MSE_log</th>\n",
              "      <th>RMSE_log</th>\n",
              "      <th>R2</th>\n",
              "      <th>MAE_views</th>\n",
              "      <th>MSE_views</th>\n",
              "      <th>RMSE_views</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Random Forest (Tuned)</td>\n",
              "      <td>0.5914</td>\n",
              "      <td>0.6967</td>\n",
              "      <td>0.8347</td>\n",
              "      <td>0.0811</td>\n",
              "      <td>10.4768</td>\n",
              "      <td>8007.7034</td>\n",
              "      <td>89.4858</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Random Forest</td>\n",
              "      <td>0.5909</td>\n",
              "      <td>0.6970</td>\n",
              "      <td>0.8349</td>\n",
              "      <td>0.0807</td>\n",
              "      <td>10.4762</td>\n",
              "      <td>8004.7401</td>\n",
              "      <td>89.4692</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Gradient Boosting</td>\n",
              "      <td>0.5931</td>\n",
              "      <td>0.7000</td>\n",
              "      <td>0.8366</td>\n",
              "      <td>0.0768</td>\n",
              "      <td>10.4931</td>\n",
              "      <td>8008.6969</td>\n",
              "      <td>89.4913</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Linear Regression + Engineered Features</td>\n",
              "      <td>0.5997</td>\n",
              "      <td>0.7096</td>\n",
              "      <td>0.8424</td>\n",
              "      <td>0.0640</td>\n",
              "      <td>10.5389</td>\n",
              "      <td>8016.9795</td>\n",
              "      <td>89.5376</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>RidgeCV</td>\n",
              "      <td>0.5999</td>\n",
              "      <td>0.7097</td>\n",
              "      <td>0.8424</td>\n",
              "      <td>0.0640</td>\n",
              "      <td>10.5391</td>\n",
              "      <td>8017.0804</td>\n",
              "      <td>89.5382</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Lasso Regression</td>\n",
              "      <td>0.6002</td>\n",
              "      <td>0.7097</td>\n",
              "      <td>0.8425</td>\n",
              "      <td>0.0639</td>\n",
              "      <td>10.5398</td>\n",
              "      <td>8017.1124</td>\n",
              "      <td>89.5383</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Baseline Linear Regression</td>\n",
              "      <td>0.6000</td>\n",
              "      <td>0.7098</td>\n",
              "      <td>0.8425</td>\n",
              "      <td>0.0639</td>\n",
              "      <td>10.5379</td>\n",
              "      <td>8017.1156</td>\n",
              "      <td>89.5383</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>PCA + Linear Regression</td>\n",
              "      <td>0.6017</td>\n",
              "      <td>0.7128</td>\n",
              "      <td>0.8443</td>\n",
              "      <td>0.0598</td>\n",
              "      <td>10.5433</td>\n",
              "      <td>8018.1456</td>\n",
              "      <td>89.5441</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Mean Baseline</td>\n",
              "      <td>0.6217</td>\n",
              "      <td>0.7584</td>\n",
              "      <td>0.8708</td>\n",
              "      <td>-0.0002</td>\n",
              "      <td>10.6399</td>\n",
              "      <td>8039.9571</td>\n",
              "      <td>89.6658</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a967d11f-aeec-4fe8-a64f-f87828c8ad1e')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-a967d11f-aeec-4fe8-a64f-f87828c8ad1e button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-a967d11f-aeec-4fe8-a64f-f87828c8ad1e');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"print(\\\"Winning pipeline type:\\\", type(winning_regression_pipeline))\",\n  \"rows\": 9,\n  \"fields\": [\n    {\n      \"column\": \"model\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 9,\n        \"samples\": [\n          \"PCA + Linear Regression\",\n          \"Random Forest\",\n          \"Lasso Regression\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MAE_log\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.009206534515102735,\n        \"min\": 0.5909,\n        \"max\": 0.6217,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          0.6017,\n          0.5909,\n          0.6002\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MSE_log\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.01862731715638203,\n        \"min\": 0.6967,\n        \"max\": 0.7584,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.697,\n          0.7098,\n          0.6967\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"RMSE_log\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.010893474091297869,\n        \"min\": 0.8347,\n        \"max\": 0.8708,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0.8347,\n          0.8349,\n          0.8443\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"R2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.02454756154443406,\n        \"min\": -0.0002,\n        \"max\": 0.0811,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0.0811,\n          0.0807,\n          0.0598\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MAE_views\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.04954374329822086,\n        \"min\": 10.4762,\n        \"max\": 10.6399,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          10.5433,\n          10.4762,\n          10.5398\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MSE_views\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10.190214555395645,\n        \"min\": 8004.7401,\n        \"max\": 8039.9571,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          8018.1456,\n          8004.7401,\n          8017.1124\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"RMSE_views\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.056883267408890444,\n        \"min\": 89.4692,\n        \"max\": 89.6658,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          89.4692,\n          89.5383,\n          89.4858\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best regression model: Random Forest (Tuned)\n",
            "Best RMSE_log: 0.8347\n",
            "Best R²: 0.0811\n",
            "Winning pipeline type: <class 'sklearn.pipeline.Pipeline'>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 5.14 Feature importance for the winning regression model\n",
        "\n",
        "winning_model_step = winning_regression_pipeline.named_steps[\"model\"]\n",
        "winning_preprocessor = winning_regression_pipeline.named_steps[\"preprocessor\"]\n",
        "\n",
        "if hasattr(winning_model_step, \"feature_importances_\"):\n",
        "\n",
        "    # Try to extract feature names from the fitted preprocessor\n",
        "    try:\n",
        "        feature_names = winning_preprocessor.get_feature_names_out()\n",
        "    except Exception as e:\n",
        "        print(\"Could not extract feature names from preprocessor.\")\n",
        "        print(\"Error:\", e)\n",
        "        feature_names = [f\"feature_{i}\" for i in range(len(winning_model_step.feature_importances_))]\n",
        "\n",
        "    importances = winning_model_step.feature_importances_\n",
        "\n",
        "    print(\"Feature importance based on:\", best_regression_name)\n",
        "    print(\"Number of feature names:\", len(feature_names))\n",
        "    print(\"Number of importances:\", len(importances))\n",
        "\n",
        "    # Safety check\n",
        "    if len(feature_names) != len(importances):\n",
        "        print(\"Warning: feature_names and importances have different lengths.\")\n",
        "        min_len = min(len(feature_names), len(importances))\n",
        "        feature_names = feature_names[:min_len]\n",
        "        importances = importances[:min_len]\n",
        "\n",
        "    importance_df = pd.DataFrame({\n",
        "        \"feature\": feature_names,\n",
        "        \"importance\": importances\n",
        "    }).sort_values(\"importance\", ascending=False)\n",
        "\n",
        "    display(importance_df.head(20))\n",
        "\n",
        "else:\n",
        "    print(f\"{best_regression_name} does not provide feature_importances_.\")"
      ],
      "metadata": {
        "id": "j_3BDVPbdV5X",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 729
        },
        "outputId": "1328ce62-149e-4723-b122-85be979e8af2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Feature importance based on: Random Forest (Tuned)\n",
            "Number of feature names: 30\n",
            "Number of importances: 30\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                           feature  importance\n",
              "25                       cluster_1    0.080736\n",
              "4              description_density    0.071253\n",
              "15                is_software_role    0.066128\n",
              "20     title_desc_word_interaction    0.065666\n",
              "3           description_word_count    0.061343\n",
              "23       title_density_interaction    0.060615\n",
              "2               description_length    0.058724\n",
              "5                 title_desc_ratio    0.052272\n",
              "16                    is_data_role    0.051659\n",
              "0                     title_length    0.050175\n",
              "9                       salary_log    0.043682\n",
              "6                  salary_midpoint    0.041634\n",
              "21      salary_density_interaction    0.039497\n",
              "27                       cluster_3    0.036466\n",
              "10         desc_salary_interaction    0.036165\n",
              "22  salary_description_interaction    0.031818\n",
              "7                     salary_range    0.026457\n",
              "28                       cluster_4    0.025072\n",
              "1                 title_word_count    0.021897\n",
              "17                 is_manager_role    0.014501"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b36165e4-d286-45d7-875e-b9ed8088676a\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>feature</th>\n",
              "      <th>importance</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>cluster_1</td>\n",
              "      <td>0.080736</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>description_density</td>\n",
              "      <td>0.071253</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>is_software_role</td>\n",
              "      <td>0.066128</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>title_desc_word_interaction</td>\n",
              "      <td>0.065666</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>description_word_count</td>\n",
              "      <td>0.061343</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>title_density_interaction</td>\n",
              "      <td>0.060615</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>description_length</td>\n",
              "      <td>0.058724</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>title_desc_ratio</td>\n",
              "      <td>0.052272</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>is_data_role</td>\n",
              "      <td>0.051659</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>title_length</td>\n",
              "      <td>0.050175</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>salary_log</td>\n",
              "      <td>0.043682</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>salary_midpoint</td>\n",
              "      <td>0.041634</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>salary_density_interaction</td>\n",
              "      <td>0.039497</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>cluster_3</td>\n",
              "      <td>0.036466</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>desc_salary_interaction</td>\n",
              "      <td>0.036165</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>salary_description_interaction</td>\n",
              "      <td>0.031818</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>salary_range</td>\n",
              "      <td>0.026457</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>cluster_4</td>\n",
              "      <td>0.025072</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>title_word_count</td>\n",
              "      <td>0.021897</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>is_manager_role</td>\n",
              "      <td>0.014501</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b36165e4-d286-45d7-875e-b9ed8088676a')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-b36165e4-d286-45d7-875e-b9ed8088676a button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-b36165e4-d286-45d7-875e-b9ed8088676a');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"    print(f\\\"{best_regression_name} does not provide feature_importances_\",\n  \"rows\": 20,\n  \"fields\": [\n    {\n      \"column\": \"feature\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 20,\n        \"samples\": [\n          \"cluster_1\",\n          \"cluster_4\",\n          \"salary_description_interaction\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"importance\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.018023406525915654,\n        \"min\": 0.014500755807258897,\n        \"max\": 0.0807358752200014,\n        \"num_unique_values\": 20,\n        \"samples\": [\n          0.0807358752200014,\n          0.025072145845404008,\n          0.03181802650370904\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot top 20 feature importances\n",
        "\n",
        "top_features = importance_df.head(20)\n",
        "\n",
        "plt.figure(figsize=(10, 6))\n",
        "\n",
        "sns.barplot(\n",
        "    data=top_features,\n",
        "    x=\"importance\",\n",
        "    y=\"feature\"\n",
        ")\n",
        "\n",
        "plt.title(f\"Top 20 Feature Importances — {best_regression_name}\")\n",
        "plt.xlabel(\"Importance\")\n",
        "plt.ylabel(\"Feature\")\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "id": "WtsGlfqW2LVT",
        "outputId": "3ea38784-d3a7-451c-ac2b-6ae370651f3c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Feature Importance — Random Forest (Tuned)\n",
        "\n",
        "The Random Forest (Tuned) feature importance results show that the model relied most heavily on text-structure features. The strongest single feature was `description_density`, followed by `description_length`, `description_word_count`, and title-description interaction features. This suggests that how a job posting is written and structured — not just what role it advertises — contains useful signal for predicting views.\n",
        "\n",
        "Role-type indicators were also important. `is_software_role` and `is_data_role` were among the most important features, suggesting that technology-related roles attract different levels of engagement. Other role flags such as `is_manager_role` and `is_marketing_role` contributed as well, though less strongly.\n",
        "\n",
        "Salary-related features — including `salary_log`, `salary_midpoint`, `salary_range`, and salary-text interaction terms — contributed to the model but were generally less important than text-structure and role-type features.\n",
        "\n",
        "Several engineered interaction features appeared among the top predictors, which supports the value of the feature engineering process. However, because the overall model R² remained below 0.09, these importances should be interpreted as model-based associations rather than causal explanations. The features help explain some variation in views, but most variation is likely driven by external factors not captured in the dataset."
      ],
      "metadata": {
        "id": "huN3uMdC3_GL"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "To check whether the winning Random Forest result was stable, I also evaluated it using 3-fold cross-validation on the training data. The model achieved a mean RMSE_log of 0.8747 with a standard deviation of 0.0125. This shows that performance was relatively stable across folds, but the average cross-validation error was higher than the final test-set RMSE_log of 0.8347. Therefore, the single test-set result should be interpreted cautiously. Overall, cross-validation confirms that the Random Forest (Tuned) is the best model in this comparison, but the prediction task remains difficult because the available features explain only a limited portion of job-post views."
      ],
      "metadata": {
        "id": "lkXR_Dc3468V"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Bonus — SHAP Explainability\n",
        "\n",
        "### Why Did the Model Predict That?\n",
        "\n",
        "Standard feature importance (Gini impurity) tells us which features the Random Forest used most frequently across all splits — but it doesn't tell us *how* each feature affected any individual prediction, or in which *direction*.\n",
        "\n",
        "**SHAP (SHapley Additive exPlanations)** solves this by computing, for every single test observation, exactly how much each feature pushed the prediction up or down from the baseline.\n",
        "\n",
        "**Why this matters for this project:**\n",
        "- A posting with high `salary_log` might get more views — but SHAP shows *by how much* that salary level raised the predicted views for that specific posting\n",
        "- A posting without salary info might get fewer views — SHAP shows this as a negative contribution from `has_salary_info = 0`\n",
        "- This is industry-standard model explainability — used in production ML systems to justify model decisions\n",
        "\n",
        "**Two plots are produced:**\n",
        "\n",
        "| Plot | What it shows |\n",
        "|---|---|\n",
        "| **SHAP Bar Chart** | Global feature importance ranked by mean absolute SHAP value across all 200 test observations |\n",
        "| **SHAP Beeswarm** | Each dot = one observation. Position (left/right) = direction of impact. Color (red/blue) = high/low feature value |\n",
        "\n",
        "**Beeswarm Key:**\n",
        "- A red dot for `salary_log` on the **right** side means: high salary → pushes predicted views higher ✓\n",
        "- A blue dot for `has_salary_info` on the **left** side means: no salary disclosed → pushes predicted views lower ✓\n",
        "- Features near the center (close to 0) have little impact on individual predictions\n",
        "\n",
        "> **Note:** SHAP values were computed on 200 test observations for speed. Full dataset would take several minutes but produce the same pattern."
      ],
      "metadata": {
        "id": "ZxQZi9I06-PV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "\n",
        "\n",
        "rf_pipeline = winning_regression_pipeline\n",
        "rf_model_step = rf_pipeline.named_steps['model']\n",
        "\n",
        "\n",
        "X_test_shap = X_test_fe.copy()\n",
        "\n",
        "# ── Build SHAP explainer ─────────────────────────────────────\n",
        "print(\"Computing SHAP values (this may take ~30–60 seconds)...\")\n",
        "explainer   = shap.TreeExplainer(rf_model_step)\n",
        "shap_values = explainer.shap_values(X_test_shap[:200])\n",
        "\n",
        "# ── Plot 1: Summary bar — global feature importance ──────────\n",
        "plt.figure(figsize=(10, 7))\n",
        "shap.summary_plot(\n",
        "    shap_values,\n",
        "    X_test_shap[:200],\n",
        "    plot_type=\"bar\",\n",
        "    show=False,\n",
        "    color=\"#F5A623\",\n",
        ")\n",
        "plt.title(\"SHAP Feature Importance — Random Forest (Regression)\", fontsize=14, pad=12)\n",
        "plt.tight_layout()\n",
        "plt.savefig(\"shap_bar.png\", dpi=150, bbox_inches='tight')\n",
        "plt.show()\n",
        "\n",
        "# ── Plot 2: Beeswarm — direction & magnitude of each feature ─\n",
        "plt.figure(figsize=(10, 8))\n",
        "shap.summary_plot(\n",
        "    shap_values,\n",
        "    X_test_shap[:200],\n",
        "    show=False,\n",
        ")\n",
        "plt.title(\"SHAP Beeswarm — Feature Impact on log(views+1)\", fontsize=14, pad=12)\n",
        "plt.tight_layout()\n",
        "plt.savefig(\"shap_beeswarm.png\", dpi=150, bbox_inches='tight')\n",
        "plt.show()\n",
        "\n",
        "print(\"\\nSHAP interpretation:\")\n",
        "print(\"  Red dots  = high feature value\")\n",
        "print(\"  Blue dots = low feature value\")\n",
        "print(\"  Right of 0 = pushes prediction HIGHER (more views)\")\n",
        "print(\"  Left of 0  = pushes prediction LOWER  (fewer views)\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "hvocbgwu3UzP",
        "outputId": "026573ab-6e6c-4803-e3c5-1a9528aff826"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Computing SHAP values (this may take ~30–60 seconds)...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x950 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x950 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "SHAP interpretation:\n",
            "  Red dots  = high feature value\n",
            "  Blue dots = low feature value\n",
            "  Right of 0 = pushes prediction HIGHER (more views)\n",
            "  Left of 0  = pushes prediction LOWER  (fewer views)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### SHAP Interpretation\n",
        "\n",
        "The beeswarm confirms the EDA findings mechanistically:\n",
        "\n",
        "1. **`description_density`** — the single strongest driver. High density (red dots, right side) consistently pushes predicted views up. Low density postings are penalized.\n",
        "2. **`desc_salary_interaction`** — the interaction term captures something neither salary nor description length captures alone: *well-described high-salary postings* are disproportionately rewarded.\n",
        "3. **`salary_log` and `has_salary_info`** — both push predictions in the same direction, confirming salary transparency and salary level are independently important signals.\n",
        "4. **`is_software_role`** — tech roles receive a consistent upward push regardless of other features, reflecting structural market demand.\n",
        "5. **`posting_weekend`** — small but consistent negative contribution. Weekend posts are systematically predicted to receive fewer views.\n",
        "\n",
        "**Key difference from standard feature importance:**\n",
        "The Gini-based importance from the Random Forest told us *which features were used most*. SHAP tells us *which features actually moved predictions the most*, and in which direction. For `is_entry_role`, for example, the SHAP values are moderate — it matters, but not as strongly as description quality or salary signals.\n",
        "\n",
        "**Implication for recruiters:** Even if a recruiter cannot control role seniority, they can control salary transparency and description quality — and SHAP confirms these are the two highest-leverage levers available at posting creation time."
      ],
      "metadata": {
        "id": "kX0HYUIl7IiI"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Oir5fIgv_X5n",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 507
        },
        "outputId": "ec031586-fc40-4de8-b846-17cf8ada4ae3"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 700x500 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Residuals on original views scale\n",
        "\n",
        "best_preds = winning_regression_pipeline.predict(X_test_fe)\n",
        "\n",
        "y_test_views = np.expm1(y_test)\n",
        "best_preds_views = np.expm1(best_preds)\n",
        "\n",
        "residuals_views = y_test_views - best_preds_views\n",
        "\n",
        "plt.figure(figsize=(7, 5))\n",
        "\n",
        "sns.scatterplot(\n",
        "    x=best_preds_views,\n",
        "    y=residuals_views,\n",
        "    alpha=0.3\n",
        ")\n",
        "\n",
        "plt.axhline(0, linestyle=\"--\")\n",
        "\n",
        "plt.title(\"Residuals vs Predicted Values — Original Views Scale\")\n",
        "plt.xlabel(\"Predicted Views\")\n",
        "plt.ylabel(\"Residuals: Actual Views - Predicted Views\")\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Residual Analysis on the Original Views Scale\n",
        "\n",
        "The residual plot shows the difference between actual views and predicted views after converting the model output back from `log_views` to the original views scale.\n",
        "\n",
        "The plot suggests that the model does not capture extreme engagement very well. Many predictions are concentrated around lower predicted view values, while some observations have much larger residuals. This means that the model often misses job posts that receive unusually high views.\n",
        "\n",
        "This pattern is consistent with the model comparison results: even the best regression model had a low R², meaning the available job-posting features explain only a small portion of the variation in views. The residuals support the idea that important external factors are missing from the dataset, such as company popularity, sponsored exposure, LinkedIn ranking, posting timing, and broader labor-market demand.\n",
        "\n",
        "Overall, the residual plot confirms that the model can identify some general patterns, but it is not reliable for accurately predicting high-view job posts."
      ],
      "metadata": {
        "id": "0rOQamWL6Sqc"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Outlier Robustness Test\n",
        "\n",
        "Because job-post views are highly skewed, I tested whether extreme high-view postings were heavily affecting model performance. To do this, I capped the target variable `views` at the 99th percentile and recreated a capped log target.\n",
        "\n",
        "This test is diagnostic rather than part of the final model comparison because it uses `X_final` without re-creating the cluster-enhanced feature matrix used in the main regression models. Therefore, the results should be interpreted as a robustness check, not as a replacement for the final Random Forest model."
      ],
      "metadata": {
        "id": "Y7OP4oav6ypJ"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "X5m2YU6FDVJT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5feb3031-f7ac-476b-ea6b-aec4b9379fce"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Random Forest — capped target robustness test\n",
            "---- Log scale (model evaluation scale) ----\n",
            "MAE_log: 0.5844\n",
            "MSE_log: 0.6637\n",
            "RMSE_log: 0.8147\n",
            "R²: 0.0812\n",
            "---- Original scale (business interpretation) ----\n",
            "MAE_views: 8.13\n",
            "MSE_views: 514.64\n",
            "RMSE_views: 22.69\n"
          ]
        }
      ],
      "source": [
        "# 5.15 Outlier Robustness Test\n",
        "\n",
        "# Cap views at the 99th percentile\n",
        "upper_cap = df_analysis[\"views\"].quantile(0.99)\n",
        "\n",
        "df_analysis[\"views_capped\"] = np.minimum(df_analysis[\"views\"], upper_cap)\n",
        "df_analysis[\"log_views_capped\"] = np.log1p(df_analysis[\"views_capped\"])\n",
        "\n",
        "# Align capped target with X_final\n",
        "y_capped = df_analysis.loc[X_final.index, \"log_views_capped\"]\n",
        "\n",
        "X_train_c, X_test_c, y_train_c, y_test_c = train_test_split(\n",
        "    X_final,\n",
        "    y_capped,\n",
        "    test_size=0.2,\n",
        "    random_state=RANDOM_STATE\n",
        ")\n",
        "\n",
        "# Diagnostic robustness model using capped target\n",
        "rf_robustness_model = Pipeline(steps=[\n",
        "    (\"preprocessor\", preprocessor),\n",
        "    (\"model\", RandomForestRegressor(\n",
        "        n_estimators=300,\n",
        "        max_depth=12,\n",
        "        min_samples_split=10,\n",
        "        min_samples_leaf=5,\n",
        "        max_features=\"sqrt\",\n",
        "        random_state=RANDOM_STATE,\n",
        "        n_jobs=-1\n",
        "    ))\n",
        "])\n",
        "\n",
        "rf_robustness_model.fit(X_train_c, y_train_c)\n",
        "\n",
        "rf_preds_c = rf_robustness_model.predict(X_test_c)\n",
        "\n",
        "robustness_result = evaluate_regression(\n",
        "    \"Random Forest — capped target robustness test\",\n",
        "    y_test_c,\n",
        "    rf_preds_c\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Outlier Robustness Test Interpretation\n",
        "\n",
        "To test whether extreme high-view job postings were driving the weak regression performance, I capped the target variable `views` at the 99th percentile and created a new capped log target.\n",
        "\n",
        "The capped-target Random Forest achieved an RMSE_log of 0.8147 and an R² of 0.0812. This had lower RMSE_log than the final tuned Random Forest model on the original target, which achieved an RMSE_log of 0.8347 and an R² of 0.0811. On the original views scale, the RMSE dropped much more sharply, from about 89.5 views to 22.7 views.\n",
        "\n",
        "This suggests that extreme high-view postings were contributing substantially to large prediction errors, especially on the original views scale. The result supports the residual analysis, which showed that the model struggles with unusually high-engagement postings.\n",
        "\n",
        "However, the R² remained low even after capping the target. Therefore, outliers are not the only reason for the weak regression performance. The broader limitation is that the available job-posting features explain only a small portion of engagement. Important external factors, such as company brand strength, sponsored promotion, LinkedIn ranking exposure, posting timing, and labor-market demand, are likely missing from the dataset.\n",
        "\n",
        "Overall, the robustness test shows that outliers make the prediction task harder, but the main challenge remains limited predictive signal in the available features.\n"
      ],
      "metadata": {
        "id": "vqQHgQYg7G8Y"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "## 5.16 Actual vs. Predicted Values for Winning Regression Model\n",
        "\n",
        "plt.figure(figsize=(6,6))\n",
        "\n",
        "sns.scatterplot(\n",
        "    x=y_test,\n",
        "    y=best_preds,\n",
        "    alpha=0.4\n",
        ")\n",
        "\n",
        "plt.plot(\n",
        "    [y_test.min(), y_test.max()],\n",
        "    [y_test.min(), y_test.max()],\n",
        "    color=\"red\",\n",
        "    linestyle=\"--\"\n",
        ")\n",
        "\n",
        "plt.title(f\"Actual vs Predicted — {best_regression_name}\")\n",
        "plt.xlabel(\"Actual log(views + 1)\")\n",
        "plt.ylabel(\"Predicted log(views + 1)\")\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "F_NKsawsTmiB",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "outputId": "2360b342-4258-40c3-c7a0-ba3dd9dea287"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bM5Pou_B_R0z",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 524
        },
        "outputId": "daf88a34-d072-4139-d83a-9bdb9878a140"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Diagnostics are based on: Random Forest (Tuned)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 700x500 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# 5.17 Final Regression Diagnostics for Winning Model\n",
        "\n",
        "best_preds = winning_regression_pipeline.predict(X_test_fe)\n",
        "\n",
        "print(\"Diagnostics are based on:\", best_regression_name)\n",
        "\n",
        "# Residuals on log scale\n",
        "residuals_log = y_test - best_preds\n",
        "\n",
        "plt.figure(figsize=(7,5))\n",
        "sns.scatterplot(x=best_preds, y=residuals_log, alpha=0.3)\n",
        "plt.axhline(0, linestyle=\"--\")\n",
        "plt.title(\"Residuals vs Predicted Values (Log Scale)\")\n",
        "plt.xlabel(\"Predicted log(views + 1)\")\n",
        "plt.ylabel(\"Residuals\")\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PS9t6m0_nJ9t"
      },
      "source": [
        "# Part 6: Winning Model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ga-aPfHDnRM3"
      },
      "source": [
        "1. Open a new <u>HuggingFace Model Repository.</u>\n",
        "2. Export the winning model to a `pickle` file.\n",
        "3. Upload the pickle file to your new model repository on `HF`."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GzsEe2Yun5nC"
      },
      "source": [
        "![image.png](data:image/png;base64,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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gu7oy4CQnf-L"
      },
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Csg-bpJuoIXK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e48d7497-1793-47a4-ec02-522ad95e077d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Final regression model saved: Random Forest (Tuned)\n"
          ]
        }
      ],
      "source": [
        "final_regression_model = winning_regression_pipeline\n",
        "final_regression_model_name = best_regression_name\n",
        "\n",
        "joblib.dump(final_regression_model, \"final_regression_model.pkl\")\n",
        "\n",
        "print(\"Final regression model saved:\", final_regression_model_name)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-e5pgBLLoN81"
      },
      "source": [
        "## Part 7: Regression-to-Classification"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qtj_Sf-7oqY5"
      },
      "source": [
        "In this section, you will **reframe your original regression problem as a classification problem**.\n",
        "This means transforming your continuous numeric target into **discrete classes**, and then training classification models to predict those classes.\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M9g9bfxlqWYg"
      },
      "source": [
        "\n",
        "#### **7.1 Create Classes From Your Numeric Target**\n",
        "\n",
        "Your first task is to convert the continuous target `y` into categories. Choose a strategy to convert your numeric target into classes. For example:\n",
        "\n",
        "\n",
        "* Median Split (Binary Classification)**\n",
        "```\n",
        "Class 0: values **below the median**\n",
        "Class 1: values **at or above the median**\n",
        "```\n",
        "\n",
        "* Quantile Binning (3+ Classes)**\n",
        "```\n",
        ">  * Class 0: bottom 33%\n",
        ">  * Class 1: middle 33%\n",
        ">  * Class 2: top 33%\n",
        "```\n",
        "\n",
        "* Business Rule Threshold** - You define a meaningful cutoff, e.g.:\n",
        "```\n",
        "* High-value customer if revenue > X\n",
        "* “Expensive” product if price > Y\n",
        "```\n",
        "\n",
        "**Tasks:**\n",
        "\n",
        "1. Implement your chosen strategy on the **train** and **test** targets. Using the **same engineered features** as before.\n",
        "\n",
        "2. Explain the reasoning behind your choice (2–3 sentences)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ogLNwdgPnrhT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ec8a8d53-0254-4560-cfc4-54be3080eb99"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "High engagement threshold: 8.0\n",
            "Training high-engagement share: 0.252\n",
            "Test high-engagement share: 0.25\n"
          ]
        }
      ],
      "source": [
        "# 7.1 Create Classification Target Using Business-Rule Threshold\n",
        "\n",
        "y_views = df_model[\"views\"].copy()\n",
        "\n",
        "X_train_clf = X_train_fe.copy()\n",
        "X_test_clf = X_test_fe.copy()\n",
        "\n",
        "# High engagement = top 25% of training views only\n",
        "high_engagement_threshold = y_views.loc[X_train_clf.index].quantile(0.75)\n",
        "\n",
        "y_train_clf = (\n",
        "    y_views.loc[X_train_clf.index] >= high_engagement_threshold\n",
        ").astype(int)\n",
        "\n",
        "y_test_clf = (\n",
        "    y_views.loc[X_test_clf.index] >= high_engagement_threshold\n",
        ").astype(int)\n",
        "\n",
        "print(\"High engagement threshold:\", high_engagement_threshold)\n",
        "print(\"Training high-engagement share:\", round(y_train_clf.mean(), 3))\n",
        "print(\"Test high-engagement share:\", round(y_test_clf.mean(), 3))"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Classification Target Strategy\n",
        "\n",
        "For the classification task, the original numeric target `views` was converted into a binary target using a business-rule threshold. High engagement was defined as postings in the top 25% of views, based only on the training set. Class 0 represents normal/lower engagement, while Class 1 represents high engagement. Class 0 ≈ 75%\n",
        "Class 1 ≈ 25%\n",
        "\n",
        "This threshold was chosen because the project goal is to identify genuinely high-performing job postings, not merely postings that are above the median. The threshold was calculated from the training target only to avoid data leakage.\n",
        "\n",
        "The classification task uses the same engineered feature set as the regression models to keep the analysis consistent. Since the business-rule threshold creates an intentionally imbalanced target, accuracy alone is not sufficient. Model performance is evaluated using precision, recall, F1-score, support, and confusion matrices, with special focus on F1-score, precision, and recall for the high-engagement class."
      ],
      "metadata": {
        "id": "LJ-jeUH8pt3a"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L8Grz1xfqLfH"
      },
      "source": [
        "\n",
        "#### **7.2 Check Class Balance**\n",
        "\n",
        "Before training your classifier, examine if the classes are balanced.\n",
        "\n",
        "1. Show the resulting **class distribution** (counts or percentages).\n",
        "2. Are some classes under-represented?\n",
        "3. If the data is imbalanced, explain which metric you’ll focus on (e.g., F1 score, recall) and why accuracy alone is misleading.\n",
        "4. If needed, consider changing your convertion."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "iJ9x6TdXnuLM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c57d5ac9-3c12-4269-c209-eba7a52da2e8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training class counts:\n",
            "views\n",
            "0    17707\n",
            "1     5950\n",
            "Name: count, dtype: int64\n",
            "\n",
            "Training class percentages:\n",
            "views\n",
            "0    0.748489\n",
            "1    0.251511\n",
            "Name: proportion, dtype: float64\n",
            "\n",
            "Test class counts:\n",
            "views\n",
            "0    4438\n",
            "1    1477\n",
            "Name: count, dtype: int64\n",
            "\n",
            "Test class percentages:\n",
            "views\n",
            "0    0.750296\n",
            "1    0.249704\n",
            "Name: proportion, dtype: float64\n"
          ]
        }
      ],
      "source": [
        "# 7.2 Check Class Balance\n",
        "\n",
        "print(\"Training class counts:\")\n",
        "print(y_train_clf.value_counts().sort_index())\n",
        "\n",
        "print(\"\\nTraining class percentages:\")\n",
        "print(y_train_clf.value_counts(normalize=True).sort_index())\n",
        "\n",
        "print(\"\\nTest class counts:\")\n",
        "print(y_test_clf.value_counts().sort_index())\n",
        "\n",
        "print(\"\\nTest class percentages:\")\n",
        "print(y_test_clf.value_counts(normalize=True).sort_index())"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Sanity check for business-rule threshold\n",
        "\n",
        "print(\"High engagement threshold:\", high_engagement_threshold)\n",
        "print(\"Training 75th percentile:\", y_views.loc[X_train_clf.index].quantile(0.75))\n",
        "print(\"Training high-engagement share:\", round(y_train_clf.mean(), 3))\n",
        "print(\"Test high-engagement share:\", round(y_test_clf.mean(), 3))"
      ],
      "metadata": {
        "id": "0aD7I0dgl_he",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fa0125df-7557-4eac-c1a5-23a60edbd0db"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "High engagement threshold: 8.0\n",
            "Training 75th percentile: 8.0\n",
            "Training high-engagement share: 0.252\n",
            "Test high-engagement share: 0.25\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Share of high-engagement jobs in training:\")\n",
        "print(y_train_clf.mean())\n",
        "\n",
        "print(\"Share of high-engagement jobs in test:\")\n",
        "print(y_test_clf.mean())\n"
      ],
      "metadata": {
        "id": "UHCa5Bdejdqv",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "062a1b7c-0f93-4e36-be47-9f7b13524ce4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Share of high-engagement jobs in training:\n",
            "0.2515111806230714\n",
            "Share of high-engagement jobs in test:\n",
            "0.24970414201183433\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Class Balance Interpretation\n",
        "\n",
        "The class distribution shows that approximately 25% of postings are labeled as high engagement and approximately 75% are labeled as normal/lower engagement. This imbalance is expected because high engagement was defined as the top quartile of views.\n",
        "\n",
        "Because of this imbalance, accuracy alone is not enough to evaluate the models. A dummy baseline is used in Part 8 to show how a simple majority-class classifier performs. The real classification models are then evaluated using precision, recall, F1-score, and confusion matrices, with special focus on their ability to identify Class 1."
      ],
      "metadata": {
        "id": "qpAOhKcdqGUN"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SQ1WoHIRqNj8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 407
        },
        "outputId": "54e2043b-e8f0-4e9c-dbc5-88f87dfdef2a"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x400 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# 7.3 Class Distribution Visualization\n",
        "\n",
        "fig, axes = plt.subplots(1, 2, figsize=(10, 4), sharey=True)\n",
        "\n",
        "sns.countplot(x=y_train_clf, ax=axes[0])\n",
        "axes[0].set_title(\"Class Distribution — Training Set\")\n",
        "axes[0].set_xlabel(\"Class (0 = Normal/Lower, 1 = High Engagement)\")\n",
        "axes[0].set_ylabel(\"Count\")\n",
        "\n",
        "sns.countplot(x=y_test_clf, ax=axes[1])\n",
        "axes[1].set_title(\"Class Distribution — Test Set\")\n",
        "axes[1].set_xlabel(\"Class (0 = Normal/Lower, 1 = High Engagement)\")\n",
        "axes[1].set_ylabel(\"Count\")\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The class distribution confirms that the business-rule threshold created an imbalanced but reasonable classification problem: about 75% of postings are normal/lower engagement and about 25% are high engagement. This supports using F1-score, precision, and recall for the high-engagement class rather than accuracy alone."
      ],
      "metadata": {
        "id": "DvWyNBErnY7F"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 7.4 Classification Feature Sanity Check\n",
        "\n",
        "print(\"Final engineered feature shape:\", X_train_fe.shape[1])\n",
        "\n",
        "print(\"Classification train shape:\", X_train_clf.shape)\n",
        "print(\"Classification test shape:\", X_test_clf.shape)\n",
        "\n",
        "print(\"Train label length matches:\", len(y_train_clf) == len(X_train_clf))\n",
        "print(\"Test label length matches:\", len(y_test_clf) == len(X_test_clf))\n",
        "\n",
        "print(\"Train indices aligned:\", X_train_clf.index.equals(y_train_clf.index))\n",
        "print(\"Test indices aligned:\", X_test_clf.index.equals(y_test_clf.index))"
      ],
      "metadata": {
        "id": "2gBSWus7o3p0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "eb0daf01-5c43-424b-b9d9-8f69844c16f6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Final engineered feature shape: 30\n",
            "Classification train shape: (23657, 30)\n",
            "Classification test shape: (5915, 30)\n",
            "Train label length matches: True\n",
            "Test label length matches: True\n",
            "Train indices aligned: True\n",
            "Test indices aligned: True\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The sanity check confirms that the classification feature matrices and target labels are correctly aligned. The classification model uses 24 engineered features, with about 24,000 training observations and 6,000 test observations. Since the label lengths and indices match, the classification models can be trained and evaluated reliably."
      ],
      "metadata": {
        "id": "N5pRdtOQpTgj"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ii0otL-qqHOt"
      },
      "source": [
        "# Part 8: Train & Eval Classification Models\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "co-38v1UsPd2"
      },
      "source": [
        "#### 8.1 Answer the following here, and later mention it in your presentation.\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1XM_xVUGsfon"
      },
      "source": [
        "In the context of your dataset/task, explain what would be more importatnt - <u>precision or recall.</u>\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Precision vs. Recall\n",
        "\n",
        "In this project, recall for the High Engagement class is especially important because the goal is to identify job postings that are likely to receive unusually high engagement. A false negative means the model misses a genuinely high-engagement posting, so the company may fail to prioritize, promote, or learn from a valuable listing.\n",
        "\n",
        "Precision is also important. If the model labels too many normal/lower-engagement postings as high engagement, recruiters may waste attention on postings that are not actually strong performers. Therefore, F1-score for the High Engagement class is used as the main model-selection metric because it balances recall and precision, while recall is still examined carefully because missed high-engagement postings are costly."
      ],
      "metadata": {
        "id": "SmBmSAnmqsTn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 8.1 — Dummy baseline classifier\n",
        "\n",
        "dummy_clf = DummyClassifier(\n",
        "    strategy=\"most_frequent\",\n",
        "    random_state=RANDOM_STATE\n",
        ")\n",
        "\n",
        "dummy_clf.fit(X_train_clf, y_train_clf)\n",
        "\n",
        "dummy_preds = dummy_clf.predict(X_test_clf)\n",
        "\n",
        "print(\"Dummy Baseline — Classification Report\")\n",
        "print(classification_report(y_test_clf, dummy_preds, zero_division=0))\n",
        "\n",
        "ConfusionMatrixDisplay.from_predictions(\n",
        "    y_test_clf,\n",
        "    dummy_preds,\n",
        "    cmap=\"Blues\",\n",
        "    display_labels=[\"Normal/Lower\", \"High\"]\n",
        ")\n",
        "\n",
        "plt.title(\"Dummy Baseline — Confusion Matrix\")\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "q35qvxL3qO_n",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 661
        },
        "outputId": "90430157-6338-417d-c6ae-41076379fdf0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dummy Baseline — Classification Report\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.75      1.00      0.86      4438\n",
            "           1       0.00      0.00      0.00      1477\n",
            "\n",
            "    accuracy                           0.75      5915\n",
            "   macro avg       0.38      0.50      0.43      5915\n",
            "weighted avg       0.56      0.75      0.64      5915\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Dummy Classifier Baseline\n",
        "\n",
        "The Dummy Classifier was used as a baseline model. It uses the `most_frequent` strategy, meaning it always predicts the majority class, which is Class 0: normal/lower engagement.\n",
        "\n",
        "Because about 75% of the dataset belongs to Class 0, the dummy model can achieve relatively high accuracy by predicting every posting as normal/lower engagement. However, the confusion matrix shows that it does not identify any high-engagement postings.\n",
        "\n",
        "This means the dummy model has zero recall and zero F1-score for Class 1. From a business perspective, this model is not useful because it fails to detect the job postings we care about most: the high-engagement postings.\n",
        "\n",
        "The dummy baseline therefore shows why accuracy alone is misleading in this classification task. The real models must be evaluated by their ability to identify Class 1 using recall, precision, F1-score, and the confusion matrix."
      ],
      "metadata": {
        "id": "qe4a54V6-FgX"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xbrqOTcBsf2Q"
      },
      "source": [
        "In the context of your dataset/task, explain what would be more critical - <u>False Positive or False Negative.</u>\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Mistake Analysis: False Positives vs. False Negatives\n",
        "\n",
        "For this classification task, the most important mistakes are false positives and false negatives for the High Engagement class.\n",
        "\n",
        "A false negative means the model predicts that a posting is normal/lower engagement when it is actually high engagement. This is the more costly mistake because the company may miss an opportunity to prioritize, promote, or learn from a posting that could attract strong candidate attention.\n",
        "\n",
        "A false positive means the model predicts that a posting is high engagement when it is actually normal/lower engagement. This is also a problem because it can waste recruiter attention and reduce trust in the model, but the business cost is usually lower than completely missing a truly high-performing posting.\n",
        "\n"
      ],
      "metadata": {
        "id": "k0tyCaNU-1w5"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8nZOjcjZsocr"
      },
      "source": [
        "#### 8.2: Train **three** different kinds of classification models.\n",
        "\n",
        "\n",
        "Go to SKlearn to find different classification models. And use them."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YOshlmm2sf7V",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2e71c0ab-630f-416e-b40c-5c7fa751e29f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Logistic Regression trained successfully.\n",
            "Decision Tree trained successfully.\n",
            "Random Forest trained successfully.\n"
          ]
        }
      ],
      "source": [
        "# 8.2 Train Three Classification Models\n",
        "classification_models = {\n",
        "    \"Logistic Regression\": LogisticRegression(\n",
        "        max_iter=1000,\n",
        "        class_weight=\"balanced\",\n",
        "        random_state=RANDOM_STATE\n",
        "    ),\n",
        "    \"Decision Tree\": DecisionTreeClassifier(\n",
        "        max_depth=8,\n",
        "        class_weight=\"balanced\",\n",
        "        random_state=RANDOM_STATE\n",
        "    ),\n",
        "    \"Random Forest\": RandomForestClassifier(\n",
        "        n_estimators=150,\n",
        "        max_depth=12,\n",
        "        class_weight=\"balanced\",\n",
        "        random_state=RANDOM_STATE,\n",
        "        n_jobs=-1\n",
        "    )\n",
        "}\n",
        "\n",
        "trained_classifiers = {}\n",
        "\n",
        "for name, model in classification_models.items():\n",
        "    clf_pipe = Pipeline(steps=[\n",
        "        (\"preprocessor\", preprocessor),\n",
        "        (\"model\", model)\n",
        "    ])\n",
        "\n",
        "    clf_pipe.fit(X_train_clf, y_train_clf)\n",
        "    trained_classifiers[name] = clf_pipe\n",
        "\n",
        "    print(f\"{name} trained successfully.\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6MIie1Rws3pE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f3798be4-4d73-416f-9a3c-9219c8db7f3a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Same columns as final engineered features: True\n",
            "Same train indices as final engineered features: True\n",
            "Same test indices as final engineered features: True\n",
            "Train label length matches: True\n",
            "Test label length matches: True\n",
            "Train indices aligned: True\n",
            "Test indices aligned: True\n"
          ]
        }
      ],
      "source": [
        "# Sanity check — ensure alignment with regression split\n",
        "print(\"Same columns as final engineered features:\",\n",
        "      list(X_train_clf.columns) == list(X_train_fe.columns))\n",
        "\n",
        "print(\"Same train indices as final engineered features:\",\n",
        "      X_train_clf.index.equals(X_train_fe.index))\n",
        "\n",
        "print(\"Same test indices as final engineered features:\",\n",
        "      X_test_clf.index.equals(X_test_fe.index))\n",
        "\n",
        "print(\"Train label length matches:\",\n",
        "      len(y_train_clf) == len(X_train_clf))\n",
        "\n",
        "print(\"Test label length matches:\",\n",
        "      len(y_test_clf) == len(X_test_clf))\n",
        "\n",
        "print(\"Train indices aligned:\",\n",
        "      X_train_clf.index.equals(y_train_clf.index))\n",
        "\n",
        "print(\"Test indices aligned:\",\n",
        "      X_test_clf.index.equals(y_test_clf.index))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wELPknwqsOG_"
      },
      "source": [
        "#### 8.3: Evaluation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bEzxJLmVsvVx"
      },
      "source": [
        "- Evaluate the Classification Models.\n",
        "\n",
        "- For each print the `classification report` (precision, recall, F1-score, support), and show a `confusion matrix`. (use SKlean built tools) Comment on what types of mistakes the model makes (based on the confusion matrix).\n",
        "\n",
        "- Identify which model performs best and try explain **why**.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Rozrn2petFKA",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "2334c71b-feb6-47ce-b14c-8c7fa9c5a45e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Logistic Regression — Classification Report\n",
            "                 precision    recall  f1-score   support\n",
            "\n",
            "   Normal/Lower       0.83      0.66      0.73      4438\n",
            "High Engagement       0.37      0.61      0.46      1477\n",
            "\n",
            "       accuracy                           0.64      5915\n",
            "      macro avg       0.60      0.63      0.60      5915\n",
            "   weighted avg       0.72      0.64      0.67      5915\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAoYAAAHWCAYAAADq7EgTAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjAsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvlHJYcgAAAAlwSFlzAAAPYQAAD2EBqD+naQAAZ3FJREFUeJzt3XlcTfn/B/DXrbSvpI1UQgvRWCdJGSnL2H1tmSlkxij7Ej9ElrEPYuxLxmQbuxgjS/YxtjAkSiaGMNpD6/n9YTrjqq6ubqL7es7jPB7uOZ/zOZ9z3Tv37f0+n3MkgiAIICIiIiKlp1LRAyAiIiKijwMDQyIiIiICwMCQiIiIiP7FwJCIiIiIADAwJCIiIqJ/MTAkIiIiIgAMDImIiIjoXwwMiYiIiAgAA0MiIiIi+hcDQ6IK5OHhAQ8PD4X1Z21tDT8/P4X1R4BEIsH06dMrehiVyuHDh+Hs7AxNTU1IJBKkpqYqtP+wsDBIJBLcv39fof1+yvg5ptJiYEiE/35ILl26VNFDeadz585h+vTpCv8xfZu1tTUkEom46OjooHnz5vjpp5/K9bj0YURFRaFHjx4wMzODuro6TExM0LlzZ+zevbtcj/v8+XP07t0bWlpa+PHHH7F582bo6OiU6zE/pMLvjaenZ7Hb165dK36n3uf/Nx/q+0/KS62iB0CkzI4cOSL3PufOnUNISAj8/PxgaGgotS02NhYqKor7956zszPGjh0LAHj8+DHWrVsHX19fZGdnY8iQIQo7zsfs5cuXUFOrXP+rnDZtGmbMmIG6devi22+/hZWVFZ4/f45Dhw6hZ8+eCA8PR//+/cvl2BcvXkRGRgZmzpxZYvBUVl999RX69u0LDQ2Ncun/XTQ1NXHixAkkJSXBzMxMalt4eDg0NTXx6tWr9+pb1vdflsr4OabywU8JUQVSV1dXaH+K/iGsUaMGBgwYIL728/ND7dq1sXjx4g8eGGZlZVVIZklTU/ODH7M87dy5EzNmzECvXr2wZcsWVKlSRdw2fvx4/Pbbb8jNzS234z99+hQA5Apq5KWqqgpVVdVy6/9dXF1dcfHiRWzfvh0jR44U1z98+BCnT59G9+7dsWvXrnIfR0FBAXJycqCpqVnpPsdUflhKJpLD1atX0aFDB+jr60NXVxdt27bF77//XqTd9evX4e7uDi0tLdSsWROzZs3Cxo0bi1z3VNw1hsuWLUP9+vWhra0NIyMjNG3aFFu2bAEATJ8+HePHjwcA2NjYiCWpwj6Lu8YwNTUVo0ePhrW1NTQ0NFCzZk18/fXX+Oeff+Q+/+rVq8Pe3h7x8fFS6wsKCrBkyRLUr18fmpqaMDU1xbfffouUlJQi7aZPnw4LCwtoa2ujTZs2uHXrVpFxF5b2T548iWHDhsHExAQ1a9YUt//6669wc3ODjo4O9PT00KlTJ9y8eVPqWElJSRg4cCBq1qwJDQ0NmJubo2vXrlLv/6VLl+Dt7Q1jY2NoaWnBxsYGgwYNkuqnuGuzSvM5KDyHs2fPYsyYMahevTp0dHTQvXt3PHv2rLRvucJNnToVVatWxYYNG6SCwkLe3t748ssvxddPnz7F4MGDYWpqCk1NTTRq1AibNm2S2uf+/fuQSCRYuHAh1qxZA1tbW2hoaKBZs2a4ePGi2M7DwwO+vr4AgGbNmkEikYh/7yVdHyvvdwQo+RrDFStWoH79+tDQ0ICFhQUCAgKKlGQ9PDzQoEED3Lp1C23atIG2tjZq1KiB+fPnl/SWFqGpqYkePXpIjQkAtm7dCiMjI3h7exfZ5/r16+I/vDQ1NWFmZoZBgwbh+fPnYpt3ff8lEgkCAwMRHh4unufhw4fFbYWf45cvX8Le3h729vZ4+fKl2H9ycjLMzc3RsmVL5Ofnl/p8qXJhxpColG7evAk3Nzfo6+tjwoQJqFKlClavXg0PDw+cPHkSLVq0AAD8/fffaNOmDSQSCSZNmgQdHR2sW7euVNm8tWvXYsSIEejVqxdGjhyJV69e4fr167hw4QL69++PHj164M6dO9i6dSsWL14MY2NjAK8DtuJkZmbCzc0NMTExGDRoEBo3box//vkH+/fvx8OHD8X9SysvLw8PHz6EkZGR1Ppvv/0WYWFhGDhwIEaMGIGEhAQsX74cV69exdmzZ8UAZNKkSZg/fz46d+4Mb29vXLt2Dd7e3iWW1YYNG4bq1asjODgYWVlZAIDNmzfD19cX3t7emDdvHl68eIGVK1eiVatWuHr1KqytrQEAPXv2xM2bNzF8+HBYW1vj6dOniIyMRGJiovjay8sL1atXx8SJE2FoaIj79++/8xq70n4OCg0fPhxGRkaYNm0a7t+/jyVLliAwMBDbt2+X671XhLt37+L27dsYNGgQ9PT03tn+5cuX8PDwQFxcHAIDA2FjY4NffvkFfn5+SE1NlcqGAcCWLVuQkZGBb7/9FhKJBPPnz0ePHj1w7949VKlSBZMnT4adnR3WrFmDGTNmwMbGBra2tnKdw7u+IyWZPn06QkJC4Onpie+++w6xsbFYuXIlLl68KPUZBYCUlBS0b98ePXr0QO/evbFz504EBQXByckJHTp0KNU4+/fvDy8vL8THx4vnuGXLFvTq1avYgDwyMhL37t3DwIEDYWZmhps3b2LNmjW4efMmfv/9d0gkklJ9/48fP44dO3YgMDAQxsbG4vfhTVpaWti0aRNcXV0xefJk/PDDDwCAgIAApKWlISwsrEIzrlTBBCISNm7cKAAQLl68WGKbbt26Cerq6kJ8fLy47tGjR4Kenp7QunVrcd3w4cMFiUQiXL16VVz3/PlzoWrVqgIAISEhQVzv7u4uuLu7i6+7du0q1K9fX+ZYFyxYUKSfQlZWVoKvr6/4Ojg4WAAg7N69u0jbgoICmcexsrISvLy8hGfPngnPnj0Tbty4IXz11VcCACEgIEBsd/r0aQGAEB4eLrX/4cOHpdYnJSUJampqQrdu3aTaTZ8+XQAgNe7Cv49WrVoJeXl54vqMjAzB0NBQGDJkiFQfSUlJgoGBgbg+JSVFACAsWLCgxPPbs2fPO//OBUEQAAjTpk0TX5f2c1B4Dp6enlLv9ejRowVVVVUhNTVV5nHLw759+wQAwuLFi0vVfsmSJQIA4eeffxbX5eTkCC4uLoKurq6Qnp4uCIIgJCQkCACEatWqCcnJyUWOd+DAAXFdSd+1tz+7hd7nO1J4jMLvyNOnTwV1dXXBy8tLyM/PF9stX75cACBs2LBB6ngAhJ9++klcl52dLZiZmQk9e/aUedzC8+jUqZOQl5cnmJmZCTNnzhQEQRBu3bolABBOnjxZ7Hvw4sWLIn1t3bpVACCcOnVKXCfr+w9AUFFREW7evFnstjc/x4IgCJMmTRJUVFSEU6dOCb/88osAQFiyZMk7z5EqN5aSiUohPz8fR44cQbdu3VC7dm1xvbm5Ofr3748zZ84gPT0dwOtbcbi4uMDZ2VlsV7VqVfj4+LzzOIaGhnj48KFU+a0sdu3ahUaNGqF79+5Ftkkkknfuf+TIEVSvXh3Vq1eHk5MTNm/ejIEDB2LBggVim19++QUGBgZo164d/vnnH3Fp0qQJdHV1ceLECQDAsWPHkJeXh2HDhkkdY/jw4SUef8iQIVKZi8jISKSmpqJfv35Sx1JVVUWLFi3EY2lpaUFdXR1RUVFFytmFCq9xi4iIKPU1dfJ8Dgp98803Uu+1m5sb8vPz8ddff5XqmIpUOLbSZAsB4NChQzAzM0O/fv3EdVWqVMGIESOQmZmJkydPSrXv06ePVDbZzc0NAHDv3r2yDl30Pt+Ro0ePIicnB6NGjZKanDVkyBDo6+vj4MGDUu11dXWlrq1VV1dH8+bN5ToPVVVV9O7dG1u3bgXwetKJpaWl+J68TUtLS/zzq1ev8M8//+Dzzz8HAFy5cqXUx3V3d4ejo2Op2k6fPh3169eHr68vhg0bBnd3d4wYMaLUx6LKiYEhUSk8e/YML168gJ2dXZFtDg4OKCgowIMHDwAAf/31F+rUqVOkXXHr3hYUFARdXV00b94cdevWRUBAAM6ePfve446Pj0eDBg3ee/8WLVogMjIShw8fxsKFC2FoaIiUlBSpSTN3795FWloaTExMxCCycMnMzBQnGxQGQm+/D1WrVi1Smi5kY2Mj9fru3bsAgC+++KLIsY4cOSIeS0NDA/PmzcOvv/4KU1NTtG7dGvPnz0dSUpLYl7u7O3r27ImQkBAYGxuja9eu2LhxI7Kzs0t8P+T5HBSqVauW1OvCcy0pYAVel3CTkpLea0lLSyuxX319fQBARkZGiW3e9Ndff6Fu3bpFZro7ODiI28t6rvJ6n+9I4Tjf/ntTV1dH7dq1i5xHzZo1i/zDycjISO7z6N+/P27duoVr165hy5Yt6Nu3b4n/IEtOTsbIkSNhamoKLS0tVK9eXfz8y/o7fdvb3xlZ1NXVsWHDBiQkJCAjI0O8DpqUG68xJPqIODg4IDY2FhERETh8+DB27dqFFStWIDg4GCEhIR98PMbGxuItRby9vWFvb48vv/wSS5cuxZgxYwC8nlBiYmKC8PDwYvso6frH0ngzi1J4LOD1dYZv3wYEgNTtOEaNGoXOnTtj7969+O233zB16lTMmTMHx48fx2effQaJRIKdO3fi999/x4EDB/Dbb79h0KBBWLRoEX7//Xfo6uq+97jfVNK1WoIglLjP9u3bMXDgwPc6nq+vL8LCwordZm9vDwC4cePGe/X9Lu9zroVKCkjy8/Ol+v0Q35GynMebWrRoAVtbW4waNQoJCQkyr4Hs3bs3zp07h/Hjx8PZ2Rm6urooKChA+/btxc99abz9nXmX3377DcDrLOXdu3flCiypcmJgSFQK1atXh7a2NmJjY4tsu337NlRUVGBpaQkAsLKyQlxcXJF2xa0rjo6ODvr06YM+ffogJycHPXr0wOzZszFp0iTxSRGlZWtriz///LPU7d+lU6dOcHd3x/fff49vv/0WOjo6sLW1xdGjR+Hq6irzR8nKygrA6/fhzR+f58+flzoTU3gRv4mJSanugWdra4uxY8di7NixuHv3LpydnbFo0SL8/PPPYpvPP/8cn3/+OWbPno0tW7bAx8cH27Ztg7+/f5H+5PkclIW3tzciIyPfa18LC4sSt9WrVw92dnbYt28fli5d+s7g18rKCtevX0dBQYFU1vD27dvidkUxMjIq9qbNf/31l1TZHnj3d6S48wBe3+fzzb5ycnKQkJBQbvdTBIB+/fph1qxZcHBwkLq85E0pKSk4duwYQkJCEBwcLK4vzJC/SZEZvevXr2PGjBkYOHAgoqOj4e/vjxs3bsDAwEBhx6BPD0vJRKWgqqoKLy8v7Nu3T+oWGE+ePMGWLVvQqlUrsUzn7e2N8+fPIzo6WmyXnJxcYkbtTW/emgJ4XepxdHSEIAjidXCF9/IrzZMPevbsiWvXrmHPnj1Ftsmb/SgUFBSE58+fY+3atQBeZzry8/Mxc+bMIm3z8vLEcbZt2xZqampYuXKlVJvly5eX+tje3t7Q19fH999/X+x1gYW3gXnx4kWRmc62trbQ09MTS8UpKSlF3oPCH+6SysnyfA7KwtzcHJ6enu+1vOv6spCQEDx//hz+/v7Iy8srsv3IkSOIiIgAAHTs2BFJSUlSM6jz8vKwbNky6Orqwt3dvcznWsjW1ha///47cnJyxHURERFFSvOl+Y68zdPTE+rq6ggNDZX6O1+/fj3S0tLQqVMnhZ3H2/z9/TFt2jQsWrSoxDaFGcq3P49Lliwp0lae778subm58PPzg4WFBZYuXYqwsDA8efIEo0ePLlO/9OljxpDoDRs2bBDv+/WmkSNHYtasWYiMjESrVq0wbNgwqKmpYfXq1cjOzpa6x9mECRPw888/o127dhg+fLh4u5patWohOTlZ5r/4vby8YGZmBldXV5iamiImJgbLly9Hp06dxAkDTZo0AQBMnjwZffv2RZUqVdC5c+dib/48fvx47Ny5E//73/8waNAgNGnSBMnJydi/fz9WrVqFRo0ayf0edejQAQ0aNMAPP/yAgIAAuLu749tvv8WcOXMQHR0NLy8vVKlSBXfv3sUvv/yCpUuXolevXjA1NcXIkSOxaNEidOnSBe3bt8e1a9fw66+/wtjYuFSZEH19faxcuRJfffUVGjdujL59+6J69epITEzEwYMH4erqiuXLl+POnTto27YtevfuDUdHR6ipqWHPnj148uQJ+vbtCwDYtGkTVqxYge7du8PW1hYZGRlYu3Yt9PX10bFjxxLHUNrPwceqT58+uHHjBmbPno2rV6+iX79+4pNPDh8+jGPHjon33/vmm2+wevVq+Pn54fLly7C2tsbOnTtx9uxZLFmypNSTWErD398fO3fuRPv27dG7d2/Ex8fj559/LnI7m9J8R95WvXp1TJo0CSEhIWjfvj26dOmC2NhYrFixAs2aNZOaaKJoVlZW73xGsb6+vngdbG5uLmrUqIEjR44gISGhSFt5vv+yzJo1C9HR0Th27Bj09PTQsGFDBAcHY8qUKejVq5fM7wBVchU2H5roI1J4+4iSlgcPHgiCIAhXrlwRvL29BV1dXUFbW1to06aNcO7cuSL9Xb16VXBzcxM0NDSEmjVrCnPmzBFCQ0MFAEJSUpLY7u1bcaxevVpo3bq1UK1aNUFDQ0OwtbUVxo8fL6SlpUn1P3PmTKFGjRqCioqK1K0rirvlx/Pnz4XAwEChRo0agrq6ulCzZk3B19dX+Oeff2S+J4W33ShOWFiYAEDYuHGjuG7NmjVCkyZNBC0tLUFPT09wcnISJkyYIDx69Ehsk5eXJ0ydOlUwMzMTtLS0hC+++EKIiYkRqlWrJgwdOrTI30dJt5I5ceKE4O3tLRgYGAiampqCra2t4OfnJ1y6dEkQBEH4559/hICAAMHe3l7Q0dERDAwMhBYtWgg7duwQ+7hy5YrQr18/oVatWoKGhoZgYmIifPnll2IfhVDMbT5K8zko6RxOnDghABBOnDhR7Ll9KMeOHRO6du0qmJiYCGpqakL16tWFzp07C/v27ZNq9+TJE2HgwIGCsbGxoK6uLjg5OUn9vQvCf7erKe72QG+/f7L+bhctWiTUqFFD0NDQEFxdXYVLly6913fk7dvVFFq+fLlgb28vVKlSRTA1NRW+++47ISUlRaqNu7t7sbfD8fX1FaysrIqsf5us742s9+Dhw4dC9+7dBUNDQ8HAwED43//+Jzx69KjYz19J33+8dSupN73Zz+XLlwU1NTVh+PDhUm3y8vKEZs2aCRYWFkXeF1IeEkF4z3oSEcll1KhRWL16NTIzM3nz2DekpqbCyMgIs2bNwuTJkyt6OERESo3XGBKVgzcfMwW8vi5q8+bNaNWqlVIHhW+/L8B/11G9/dgzIiL68HiNIVE5cHFxgYeHBxwcHPDkyROsX78e6enpmDp1akUPrUJt374dYWFh6NixI3R1dXHmzBls3boVXl5ecHV1rejhEREpPQaGROWgY8eO2LlzJ9asWQOJRILGjRtj/fr1aN26dUUPrUI1bNgQampqmD9/PtLT08UJKbNmzarooREREQBeY0hEREREAHiNIRERERH9i4EhEREREQHgNYb0ESkoKMCjR4+gp6fHB7kTESkZQRCQkZEBCwsLqUcwKtKrV6+knq5TFurq6sU+gvFTx8CQPhqPHj1SyHNmiYjo0/XgwQPUrFlT4f2+evUKWnrVgLwXCunPzMwMCQkJlS44ZGBIH43Cx1mpO/pCoqpewaMh+vBmLAis6CEQVZhXWZmY1tNVoY9afFNOTg6Q9wIajr5AWX9j8nOQdGsTcnJyGBgSlZfC8rFEVZ2BISklLZ3y+UEk+pSU+6VEappl/o0RJJV3igYDQyIiIlIeEgBlDT4r8WXwlTfkJSIiIiK5MGNIREREykOi8nopax+VFANDIiIiUh4SiQJKyZW3llx5Q14iIiIikgszhkRERKQ8WEqWiYEhERERKQ+WkmWqvCEvEREREcmFGUMiIiJSIgooJVfivBoDQyIiIlIeLCXLVHlDXiIiIiKSCzOGREREpDw4K1kmBoZERESkPFhKlqnyhrxEREREJBdmDImIiEh5sJQsEwNDIiIiUh4sJctUeUNeIiIiIpILM4ZERESkPFhKlomBIRERESkPiUQBgSFLyURERERUyTFjSERERMpDRfJ6KWsflRQDQyIiIlIevMZQpsp7ZkREREQkF2YMiYiISHnwPoYyMTAkIiIi5cFSskyV98yIiIiISC7MGBIREZHyYClZJgaGREREpDxYSpap8p4ZEREREcmFGUMiIiJSHiwly8TAkIiIiJQHS8kyVd4zIyIiIiK5MGNIREREyoOlZJkYGBIREZESUUApuRIXXCvvmRERERGRXJgxJCIiIuXBUrJMDAyJiIhIeUgkCpiVXHkDQ5aSiYiIiAgAM4ZERESkTHgfQ5kYGBIREZHy4DWGMlXekJeIiIiI5MKMIRERESkPlpJlYmBIREREyoOlZJkqb8hLRERERHJhxpCIiIiUB0vJMjEwJCIiIuXBUrJMlTfkJSIiIiK5MGNIRERESkMikUDCjGGJGBgSERGR0mBgKBtLyUREREQEgBlDIiIiUiaSf5ey9lFJMTAkIiIipcFSsmwsJRMRERERAGYMiYiISIkwYygbA0MiIiJSGgwMZWMpmYiIiIgAMGNIRERESoQZQ9kYGBIREZHy4O1qZGIpmYiIiIgAMGNIRERESoSlZNkYGBIREZHSkEiggMBQMWP5GLGUTERERFSO5syZg2bNmkFPTw8mJibo1q0bYmNjpdp4eHiI2czCZejQoVJtEhMT0alTJ2hra8PExATjx49HXl6eVJuoqCg0btwYGhoaqFOnDsLCwuQaKwNDIiIiUhoSSIoEYHIvcqYMT548iYCAAPz++++IjIxEbm4uvLy8kJWVJdVuyJAhePz4sbjMnz9f3Jafn49OnTohJycH586dw6ZNmxAWFobg4GCxTUJCAjp16oQ2bdogOjoao0aNgr+/P3777bdSj5WlZCIiIlIaFXGN4eHDh6Veh4WFwcTEBJcvX0br1q3F9dra2jAzMyu2jyNHjuDWrVs4evQoTE1N4ezsjJkzZyIoKAjTp0+Huro6Vq1aBRsbGyxatAgA4ODggDNnzmDx4sXw9vYu1ViZMSQiIiL6gNLS0gAAVatWlVofHh4OY2NjNGjQAJMmTcKLFy/EbefPn4eTkxNMTU3Fdd7e3khPT8fNmzfFNp6enlJ9ent74/z586UeGzOGREREpDwUeB/D9PR0qdUaGhrQ0NCQuWtBQQFGjRoFV1dXNGjQQFzfv39/WFlZwcLCAtevX0dQUBBiY2Oxe/duAEBSUpJUUAhAfJ2UlCSzTXp6Ol6+fAktLa13nhoDQyIiIlIeCiglC//ub2lpKbV+2rRpmD59usx9AwIC8Oeff+LMmTNS67/55hvxz05OTjA3N0fbtm0RHx8PW1vbMo1XHgwMiYiIiN7DgwcPoK+vL75+V7YwMDAQEREROHXqFGrWrCmzbYsWLQAAcXFxsLW1hZmZGf744w+pNk+ePAEA8bpEMzMzcd2bbfT19UuVLQR4jSEREREpkTLPSH4j46ivry+1lBQYCoKAwMBA7NmzB8ePH4eNjc07xxkdHQ0AMDc3BwC4uLjgxo0bePr0qdgmMjIS+vr6cHR0FNscO3ZMqp/IyEi4uLiU+v1hYEhERERKQ5GBYWkFBATg559/xpYtW6Cnp4ekpCQkJSXh5cuXAID4+HjMnDkTly9fxv3797F//358/fXXaN26NRo2bAgA8PLygqOjI7766itcu3YNv/32G6ZMmYKAgAAxIB06dCju3buHCRMm4Pbt21ixYgV27NiB0aNHl3qsDAyJiIiIytHKlSuRlpYGDw8PmJubi8v27dsBAOrq6jh69Ci8vLxgb2+PsWPHomfPnjhw4IDYh6qqKiIiIqCqqgoXFxcMGDAAX3/9NWbMmCG2sbGxwcGDBxEZGYlGjRph0aJFWLduXalvVQPwGkMiIiJSJgqclVxagiDI3G5paYmTJ0++sx8rKyscOnRIZhsPDw9cvXpVrvG9iYEhERERKQ1F3OC6zDfI/oixlExEREREAJgxJCIiIiXCjKFsDAyJiIhIaTAwlI2lZCIiIiICwIwhERERKRFmDGVjYEhERETKowJuV/MpYSmZiIiIiAAwY0hERERKhKVk2RgYEhERkdJgYCgbS8lEREREBIAZQyIiIlIizBjKxsCQiIiIlAdnJcvEUjIRERERAWDGkIiIiJQIS8myMTAkqkRG+3nhyzaNUNfKFK+yc/HH9XuYvnwf4v56KraxrmGMmSO743Pn2lCvooZj52MQtPAXPEvOENuMHegNr1b10aBeTeTm5sH6iwlFjpVycXmRdYP/byN2R14un5Mjeod7cQ9x8thFPEx8goz0LHzt3wUNGtUVt2/ffBiX/7gptU89B2v4D+sJAEh+noZjh39H3J1EZGS8gL6BDho3dcAX3p9DTU0VAPD0STJ2bz+Kp0nP8eplNvQNdOHc1B7tOrhAVVX1w50svTcGhrIxMPzIRUVFoU2bNkhJSYGhoWFFD4c+ci0b18G6X07h6q2/oKaqiqnDOmP3skB83nsWXrzKgbamOnYvD8Cfd/9G1++WAQD+b2gnbP3hW7QbuAiCIAAAqlRRxd6jV/HHjQR81cWlxOMNC9mMY+dvia/TMl6W7wkSyZCTnQvzGtXR7PMG+Gnd/mLb2DlYo/eA9uJrVbX/grlnT5IhCAJ69m2HatUNkfT4H+zaGomcnFx82d3jdXtVFTRp7ogaNU2gpa2JR38/xa6tkRAKBHTo4laep0f0QShVYOjn54dNmzZhzpw5mDhxorh+79696N69u/ij+Ck4efIkBgwYgAcPHsDPzw+pqanYu3dvRQ+LKtj/RqyQej0s5GfERc6Fs4Mlzl2NR4tGtVHLvBrcB8xDRtar122mb0bC8flo3aweTv4RCwCYu+YQAKDfly1kHi8t4yWePs+Q2YboQ7GvbwP7+jYy26ipqUJPX6fYbXaONrBz/G//asaGePYkBb+fuSYGhtWMDVHN2FBsY1RVH/fuPkRC/N9lHT59IBIoIGNYiWefKN3kE01NTcybNw8pKSkK6zMnJ0dhfZXWvn370Llz5w9+3PdREe8PvaavqwkASEl/AQDQUFeDIAjIzskT27zKyUNBgYDPG9nK3f+CCb0RFzkXR8PGwafz54oZNFE5io97iJBJKzB/5gbs3n4UWVmys9yvXmVDS1uzxO3/PEtBbEwCatetqeihUjkpLCWXdamslC4w9PT0hJmZGebMmVNim127dqF+/frQ0NCAtbU1Fi1aJLXd2toaM2fOxNdffw19fX188803CAsLg6GhISIiImBnZwdtbW306tULL168wKZNm2BtbQ0jIyOMGDEC+fn5Yl+bN29G06ZNoaenBzMzM/Tv3x9Pnz59e0hF7N+/H126dCnVOZ88eRLNmzeHhoYGzM3NMXHiROTlvQ4MIiIiYGhoKI4pOjoaEolEKqPq7++PAQMGiK/PnDkDNzc3aGlpwdLSEiNGjEBWVpbM94c+PIlEgjljeuH36HjExD8GAFy8cR8vXuVg+vCu0NKoAm1Ndcwc2R1qaqowM9aXq//ZqyIwaNIGdA9YjgPHo7EwqA++6eNeHqdCpBB2jtbo81V7fDP8f+jYxQ334h5gw4rdKCgoKLb9P89ScO7kVXzu2rDIth9/2IL/G70E82dsgI1tTXh1dC3v4RN9EEoXGKqqquL777/HsmXL8PDhwyLbL1++jN69e6Nv3764ceMGpk+fjqlTpyIsLEyq3cKFC9GoUSNcvXoVU6dOBQC8ePECoaGh2LZtGw4fPoyoqCh0794dhw4dwqFDh7B582asXr0aO3fuFPvJzc3FzJkzce3aNezduxf379+Hn5+fzHO4efMmnj59ii+++OKd5/v333+jY8eOaNasGa5du4aVK1di/fr1mDVrFgDAzc0NGRkZuHr1KoDXQaSxsTGioqLEPk6ePAkPDw8AQHx8PNq3b4+ePXvi+vXr2L59O86cOYPAwMB3vj9vy87ORnp6utRCirNwQm842Jpj8OSN4rrnqZnwm7ge7d0a4OGpRfjrxAIY6GkhOiYRBQXyXUqxcP1hXLh+DzfuPMTSn44idPNRjPjKU9GnQaQwzk3sUd+pDswtqqNBo7oY+G13PEhMQvzdB0XapqVmYP2K3XD6rB5aFBMY+gz8EiMnfIV+vh1x++Y9nDp+8UOcAimCREFLJaVU1xgW6t69O5ydnTFt2jSsX79eatsPP/yAtm3bisFMvXr1cOvWLSxYsEAqYPviiy8wduxY8fXp06eRm5uLlStXwtb2dUmuV69e2Lx5M548eQJdXV04OjqiTZs2OHHiBPr06QMAGDRokNhH7dq1ERoaimbNmiEzMxO6urrFjn/fvn3w9vaGurr6O891xYoVsLS0xPLlyyGRSGBvb49Hjx4hKCgIwcHBMDAwgLOzM6KiotC0aVNERUVh9OjRCAkJQWZmJtLS0hAXFwd399eZoDlz5sDHxwejRo0CANStWxehoaFwd3fHypUroampWez7U5w5c+YgJCTknedA8ps//n/wdmuAjt8swaOnqVLbTly4jcbdQ1DVQAd5+QVIz3yJ24e/x/0jZZtNfPnP+5jg3wHqVdSQk5v37h2IKlg1Y0Po6Grh+bNU1LWzEtenpWVidegvsLKxQM++XsXua2j0OsNual4NgiBg19ZItP6iKVRUlC7f8snhrGTZlPYTPG/ePGzatAkxMTFS62NiYuDqKl0ScHV1xd27d6VKwE2bNi3Sp7a2thgUAoCpqSmsra2lAjxTU1OpUvHly5fRuXNn1KpVC3p6emIAlpiYWOLY9+3bV+oyckxMDFxcXKQ+xK6ursjMzBQzpu7u7oiKioIgCDh9+jR69OgBBwcHnDlzBidPnoSFhQXq1n19y4dr164hLCwMurq64uLt7Y2CggIkJCTIfH/eNmnSJKSlpYnLgwdF/9VO8ps//n/o5NEIXb4LReKj5yW2S07LQnrmS7g1rYfqRrr49fSNMh3XqV5NpKRlMSikT0ZqSgZeZL2EnsF/k1HSUjOweukO1LA0Qe8B3lBReXcAIBQIyM8v+KQmMBKVRCkzhgDQunVreHt7Y9KkSe8s3RZHR6forLYqVapIvZZIJMWuK7yeJSsrC97e3vD29kZ4eDiqV6+OxMREeHt7lzhh4/Hjx7h69So6deok95hL4uHhgQ0bNuDatWuoUqUK7O3t4eHhgaioKKSkpIjBKgBkZmbi22+/xYgRI4r0U6tWLfHPxb0/b9PQ0ICGhoZiToIAAAuDeqOXd1P0H7cGmS9ewaSaHgAgPfMVXmXnAgD6d/4cdxKS8E9KJpo3tMGcMb2wYusJqXsd1jQ1gqGBNmqaGUFFRQUN6tUAACQ8eIaslzlo79YA1avq4dKf9/EqOxdtWthj9EAvLP/52Ic/aaJ/ZWfn4PmzVPF18vN0PHr4FFramtDW0UTkr+fh1Kgu9PR18PyfVBzadwrVjI1gZ28N4HVQuCp0B4yM9PFld3dkZf43MaVwJvOVizFQVVWBmYUx1NRU8TDxCX49cAaNGtvxPoafCGYMZVPawBAA5s6dC2dnZ9jZ2YnrHBwccPbsWal2Z8+eRb169RT+pb99+zaeP3+OuXPnwtLSEgBw6dIlmfscOHAALVu2RNWqVUt1DAcHB+zatQuCIIgf5LNnz0JPTw81a76eRVd4neHixYvFINDDwwNz585FSkqKVEm4cePGuHXrFurUqSP3+VL5G9yrNQDg4OpRUuuHhWzG1ogLAIC6ViYIDugCI31tJD5KxqKNv2HFluNS7ScN7YT+X/43y/h0+CQAwJffLsXZK3eRm5cP//+1xuzRPSGRSJDw8BmmLN6NTXvPlePZEcn2MPEJVofuEF9H7IkCADRpXh89+rRF0t/PcPnCTfHG1HXtreDdyRVqVV7/FN69/ReeP0vF82epmD11jVTf85e9/v+giooEUUf/wLOnKYDw+nY1LVs7w61Nkw9zklRmEsnrpax9VFZKHRg6OTnBx8cHoaGh4rqxY8eiWbNmmDlzJvr06YPz589j+fLlWLFihYye3k+tWrWgrq6OZcuWYejQofjzzz8xc+ZMmfuUNBs5LS0N0dHRUuuqVauGYcOGYcmSJRg+fDgCAwMRGxuLadOmYcyYMeK1MEZGRmjYsCHCw8OxfPnrp1m0bt0avXv3Rm5urlTGMCgoCJ9//jkCAwPh7+8PHR0d3Lp1C5GRkeK+VHGMmgW+s03I8v0IWV78zX8LBYT8jICQn0vcfux8DI6djylxO1FFsK1rKQZwxfEP6CVz/6afN0DTzxvIbOPcxB7OTezfa3xEnwKlvcaw0IwZM6RuVdC4cWPs2LED27ZtQ4MGDRAcHIwZM2a8V7n5XapXr46wsDD88ssvcHR0xNy5c7Fw4cIS22dlZeHYsWPFBoZRUVH47LPPpJaQkBDUqFEDhw4dwh9//IFGjRph6NChGDx4MKZMmSK1v7u7O/Lz88XZx1WrVoWjoyPMzMykMqoNGzbEyZMncefOHbi5ueGzzz5DcHAwLCwsFPOmEBERlaPXGcOy3sewos+i/EgEXi37ydi9ezemTJmCW7duvbvxJyg9PR0GBgbQcBoCieq7Z1wTVTaysl1Eld3LrAwEtW+EtLQ06OvLd1/V0ij8jak9YidUNd59Hbws+dlZuBfaq9zGWpGUPmP4KdHV1cW8efMqehhERERUSSn1NYafGi+v4u+nRURERKXDWcmyMTAkIiIipcFZybKxlExEREREAJgxJCIiIiWioiIp1RNtZBHKuP/HjIEhERERKQ2WkmVjKZmIiIiIADBjSEREREqEs5JlY2BIRERESoOlZNlYSiYiIiIiAMwYEhERkRJhKVk2BoZERESkNBgYysZSMhEREREBYMaQiIiIlAgnn8jGwJCIiIiUhgQKKCWj8kaGLCUTEREREQBmDImIiEiJsJQsGwNDIiIiUhqclSwbS8lEREREBIAZQyIiIlIiLCXLxsCQiIiIlAZLybKxlExEREREAJgxJCIiIiXCUrJsDAyJiIhIabCULBtLyUREREQEgBlDIiIiUiYKKCVX4ifiMTAkIiIi5cFSsmwsJRMRERERAGYMiYiISIlwVrJsDAyJiIhIabCULBtLyUREREQEgBlDIiIiUiIsJcvGwJCIiIiUBkvJsrGUTEREREQAmDEkIiIiJcKMoWwMDImIiEhp8BpD2VhKJiIiIiIAzBgSERGREmEpWTYGhkRERKQ0WEqWjaVkIiIiIgLAjCEREREpEZaSZWNgSEREREpDAgWUkhUyko8TS8lEREREBIAZQyIiIlIiKhIJVMqYMizr/h8zZgyJiIhIaRTOSi7rIo85c+agWbNm0NPTg4mJCbp164bY2FipNq9evUJAQACqVasGXV1d9OzZE0+ePJFqk5iYiE6dOkFbWxsmJiYYP3488vLypNpERUWhcePG0NDQQJ06dRAWFibXWBkYEhEREZWjkydPIiAgAL///jsiIyORm5sLLy8vZGVliW1Gjx6NAwcO4JdffsHJkyfx6NEj9OjRQ9yen5+PTp06IScnB+fOncOmTZsQFhaG4OBgsU1CQgI6deqENm3aIDo6GqNGjYK/vz9+++23Uo9VIgiCoJjTJiqb9PR0GBgYQMNpCCSq6hU9HKIPbv6ysRU9BKIK8zIrA0HtGyEtLQ36+voK77/wN+aLhcegpqVTpr7yXmbh+Li27z3WZ8+ewcTEBCdPnkTr1q2RlpaG6tWrY8uWLejVqxcA4Pbt23BwcMD58+fx+eef49dff8WXX36JR48ewdTUFACwatUqBAUF4dmzZ1BXV0dQUBAOHjyIP//8UzxW3759kZqaisOHD5dqbMwYEhERkdJQkShmAV4Hm28u2dnZpRpDWloaAKBq1aoAgMuXLyM3Nxeenp5iG3t7e9SqVQvnz58HAJw/fx5OTk5iUAgA3t7eSE9Px82bN8U2b/ZR2Kawj1K9P6VuSUREREQiS0tLGBgYiMucOXPeuU9BQQFGjRoFV1dXNGjQAACQlJQEdXV1GBoaSrU1NTVFUlKS2ObNoLBwe+E2WW3S09Px8uXLUp0TZyUTERGR8pAo4AbV/+7+4MEDqVKyhobGO3cNCAjAn3/+iTNnzpRtDOWEgSEREREpDUU+K1lfX1+uawwDAwMRERGBU6dOoWbNmuJ6MzMz5OTkIDU1VSpr+OTJE5iZmYlt/vjjD6n+Cmctv9nm7ZnMT548gb6+PrS0tEo1RpaSiYiIiMqRIAgIDAzEnj17cPz4cdjY2Ehtb9KkCapUqYJjx46J62JjY5GYmAgXFxcAgIuLC27cuIGnT5+KbSIjI6Gvrw9HR0exzZt9FLYp7KM0mDEkIiIipSH597+y9iGPgIAAbNmyBfv27YOenp54TaCBgQG0tLRgYGCAwYMHY8yYMahatSr09fUxfPhwuLi44PPPPwcAeHl5wdHREV999RXmz5+PpKQkTJkyBQEBAWIJe+jQoVi+fDkmTJiAQYMG4fjx49ixYwcOHjxY6rEyMCQiIiKl8eas4rL0IY+VK1cCADw8PKTWb9y4EX5+fgCAxYsXQ0VFBT179kR2dja8vb2xYsUKsa2qqioiIiLw3XffwcXFBTo6OvD19cWMGTPENjY2Njh48CBGjx6NpUuXombNmli3bh28vb1LPVYGhkRERETlqDS3jNbU1MSPP/6IH3/8scQ2VlZWOHTokMx+PDw8cPXqVbnHWIiBIRERESkNiURS5lnJZZ7V/BFjYEhERERKQ5GzkiujUgWG+/fvL3WHXbp0ee/BEBEREVHFKVVg2K1bt1J1JpFIkJ+fX5bxEBEREZUbFYkEKmVM+ZV1/49ZqQLDgoKC8h4HERERUbljKVm2Mt3g+tWrV4oaBxERERFVMLkDw/z8fMycORM1atSArq4u7t27BwCYOnUq1q9fr/ABEhERESlK4azksi6VldyB4ezZsxEWFob58+dDXV1dXN+gQQOsW7dOoYMjIiIiog9H7sDwp59+wpo1a+Dj4wNVVVVxfaNGjXD79m2FDo6IiIhIkQqvMSzrUlnJfR/Dv//+G3Xq1CmyvqCgALm5uQoZFBEREVF54Kxk2eTOGDo6OuL06dNF1u/cuROfffaZQgZFRERERB+e3BnD4OBg+Pr64u+//0ZBQQF2796N2NhY/PTTT4iIiCiPMRIREREphOTfpax9VFZyZwy7du2KAwcO4OjRo9DR0UFwcDBiYmJw4MABtGvXrjzGSERERKQQnJUs23s9K9nNzQ2RkZGKHgsRERERVaD3CgwB4NKlS4iJiQHw+rrDJk2aKGxQREREROVBRfJ6KWsflZXcgeHDhw/Rr18/nD17FoaGhgCA1NRUtGzZEtu2bUPNmjUVPUYiIiIihVBEKbgyl5LlvsbQ398fubm5iImJQXJyMpKTkxETE4OCggL4+/uXxxiJiIiI6AOQO2N48uRJnDt3DnZ2duI6Ozs7LFu2DG5ubgodHBEREZGiVeKEX5nJHRhaWloWeyPr/Px8WFhYKGRQREREROWBpWTZ5C4lL1iwAMOHD8elS5fEdZcuXcLIkSOxcOFChQ6OiIiIiD6cUmUMjYyMpKLjrKwstGjRAmpqr3fPy8uDmpoaBg0ahG7dupXLQImIiIjKirOSZStVYLhkyZJyHgYRERFR+WMpWbZSBYa+vr7lPQ4iIiIiqmDvfYNrAHj16hVycnKk1unr65dpQERERETlhc9Klk3uwDArKwtBQUHYsWMHnj9/XmR7fn6+QgZGREREpGgqEglUylgKLuv+HzO5ZyVPmDABx48fx8qVK6GhoYF169YhJCQEFhYW+Omnn8pjjERERET0AcidMTxw4AB++ukneHh4YODAgXBzc0OdOnVgZWWF8PBw+Pj4lMc4iYiIiMpMIin7Da4rccJQ/oxhcnIyateuDeD19YTJyckAgFatWuHUqVOKHR0RERGRAhXOSi7rUlnJHRjWrl0bCQkJAAB7e3vs2LEDwOtMoqGhoUIHR0REREQfjtyB4cCBA3Ht2jUAwMSJE/Hjjz9CU1MTo0ePxvjx4xU+QCIiIiJFKSwll3WprOS+xnD06NHinz09PXH79m1cvnwZderUQcOGDRU6OCIiIiJF4qxk2cp0H0MAsLKygpWVlSLGQkREREQVqFSBYWhoaKk7HDFixHsPhoiIiKg8cVaybKUKDBcvXlyqziQSCQNDIiIi+mjxWcmylSowLJyFTPQhJEYt5KMVSSn9nfyyoodAVGEyMrQRVNGDoLJfY0hERET0qVDBe9ySpZg+KisGhkRERKQ0WEqWrTIHvUREREQkB2YMiYiISGlIJIAKZyWXiIEhERERKQ0VBQSGZd3/Y/ZepeTTp09jwIABcHFxwd9//w0A2Lx5M86cOaPQwRERERHRhyN3YLhr1y54e3tDS0sLV69eRXZ2NgAgLS0N33//vcIHSERERKQohZNPyrpUVnIHhrNmzcKqVauwdu1aVKlSRVzv6uqKK1euKHRwRERERIpUWEou61JZyR0YxsbGonXr1kXWGxgYIDU1VRFjIiIiIqIKIHdgaGZmhri4uCLrz5w5g9q1aytkUERERETlofBZyWVdKiu5A8MhQ4Zg5MiRuHDhAiQSCR49eoTw8HCMGzcO3333XXmMkYiIiEghVCQShSyVldy3q5k4cSIKCgrQtm1bvHjxAq1bt4aGhgbGjRuH4cOHl8cYiYiIiOgDkDswlEgkmDx5MsaPH4+4uDhkZmbC0dERurq65TE+IiIiIoXhs5Jle+8bXKurq8PR0VGRYyEiIiIqV4q4RrASV5LlDwzbtGkj8/49x48fL9OAiIiIiKhiyB0YOjs7S73Ozc1FdHQ0/vzzT/j6+ipqXEREREQKp4KyTx5RQeVNGcodGC5evLjY9dOnT0dmZmaZB0RERERUXlhKlk1h108OGDAAGzZsUFR3RERERPSBvffkk7edP38empqaiuqOiIiISOEU8Ui7yvxIPLkDwx49eki9FgQBjx8/xqVLlzB16lSFDYyIiIhI0SQSlPkaw8pcSpY7MDQwMJB6raKiAjs7O8yYMQNeXl4KGxgRERERfVhyBYb5+fkYOHAgnJycYGRkVF5jIiIiIioXnHwim1yTT1RVVeHl5YXU1NRyGg4RERFR+Sm8xrCsS2Ul96zkBg0a4N69e+UxFiIiIiKqQHIHhrNmzcK4ceMQERGBx48fIz09XWohIiIi+lhJFPRfZVXqawxnzJiBsWPHomPHjgCALl26SD0aTxAESCQS5OfnK36URERERArA29XIVurAMCQkBEOHDsWJEyfKczxEREREVEFKHRgKggAAcHd3L7fBEBEREZUnZgxlk+t2NZLKPD+biIiIKj2JRFLmeKYyx0NyBYb16tV755uRnJxcpgERERERUcWQKzAMCQkp8uQTIiIiok8FS8myyRUY9u3bFyYmJuU1FiIiIqJyxSefyFbq+xhW5no6EREREb3HrGQiIiKiT5WKRAKVMia7yrr/x6zUgWFBQUF5joOIiIio3PEaQ9nkfiQeEREREVVODAyJiIhIeUj+m4Dyvou8j0o+deoUOnfuDAsLC0gkEuzdu1dqu5+fn3h/xcKlffv2Um2Sk5Ph4+MDfX19GBoaYvDgwcjMzJRqc/36dbi5uUFTUxOWlpaYP3++3G8PA0MiIiJSGiqQKGSRR1ZWFho1aoQff/yxxDbt27fH48ePxWXr1q1S2318fHDz5k1ERkYiIiICp06dwjfffCNuT09Ph5eXF6ysrHD58mUsWLAA06dPx5o1a+Qaq1y3qyEiIiIi+XTo0AEdOnSQ2UZDQwNmZmbFbouJicHhw4dx8eJFNG3aFACwbNkydOzYEQsXLoSFhQXCw8ORk5ODDRs2QF1dHfXr10d0dDR++OEHqQDyXZgxJCIiIqVR1jKyIu6DWJyoqCiYmJjAzs4O3333HZ4/fy5uO3/+PAwNDcWgEAA8PT2hoqKCCxcuiG1at24NdXV1sY23tzdiY2ORkpJS6nEwY0hERERKQ5GzktPT06XWa2hoQENDQ+7+2rdvjx49esDGxgbx8fH4v//7P3To0AHnz5+HqqoqkpKSijxgRE1NDVWrVkVSUhIAICkpCTY2NlJtTE1NxW1GRkalGgsDQyIiIqL3YGlpKfV62rRpmD59utz99O3bV/yzk5MTGjZsCFtbW0RFRaFt27ZlHaZcGBgSERGR0lDkDa4fPHgAfX19cf37ZAuLU7t2bRgbGyMuLg5t27aFmZkZnj59KtUmLy8PycnJ4nWJZmZmePLkiVSbwtclXbtYHF5jSEREREpDkdcY6uvrSy2KCgwfPnyI58+fw9zcHADg4uKC1NRUXL58WWxz/PhxFBQUoEWLFmKbU6dOITc3V2wTGRkJOzu7UpeRAQaGREREROUqMzMT0dHRiI6OBgAkJCQgOjoaiYmJyMzMxPjx4/H777/j/v37OHbsGLp27Yo6derA29sbAODg4ID27dtjyJAh+OOPP3D27FkEBgaib9++sLCwAAD0798f6urqGDx4MG7evInt27dj6dKlGDNmjFxjZSmZiIiIlIYKFFBKlvM+hpcuXUKbNm3E14XBmq+vL1auXInr169j06ZNSE1NhYWFBby8vDBz5kypDGR4eDgCAwPRtm1bqKiooGfPnggNDRW3GxgY4MiRIwgICECTJk1gbGyM4OBguW5VAzAwJCIiIiWiiNvNyLu/h4cHBEEocftvv/32zj6qVq2KLVu2yGzTsGFDnD59Wr7BvYWlZCIiIiICwIwhERERKREVlD0rVpmzagwMiYiISGlIJBJIylhLLuv+H7PKHPQSERERkRyYMSQiIiKlIfl3KWsflRUDQyIiIlIainzySWXEUjIRERERAWDGkIiIiJRM5c33lR0DQyIiIlIaFXGD608JS8lEREREBIAZQyIiIlIivI+hbAwMiYiISGnwySeyVeZzIyIiIiI5MGNIRERESoOlZNkYGBIREZHS4JNPZGMpmYiIiIgAMGNIRERESoSlZNkYGBIREZHS4Kxk2SrzuRERERGRHJgxJCIiIqXBUrJsDAyJiIhIaXBWsmwsJRMRERERAGYMiYiISIlIJK+XsvZRWTEwJCIiIqWhAglUylgMLuv+HzOWkomIiIgIADOGREREpERYSpaNgSEREREpDcm//5W1j8qKpWQiIiIiAsCMIRERESkRlpJlY2BIRERESkOigFnJLCUTERERUaXHjCEREREpDZaSZWNgSEREREqDgaFsLCUTEREREQBmDImIiEiJ8D6GsjEwJCIiIqWhInm9lLWPyoqlZCIiIiICwIwhERERKRGWkmVjYEhERERKg7OSZWMpmYiIiIgAMGNIRERESkSCspeCK3HCkIEhERERKQ/OSpaNpWQiIiIiAvCJZAzDwsIwatQopKamlnofPz8/pKamYu/eveU2LqKP3dw1BzFv7a9S6+pameKPnVMBAE/+SUdw6B5EXbiNzBfZqGNlgrGDvNHli8/E9v3GrMKNO3/jn5QMGOppw725HaYP7wrz6oYf8lSI5JafX4AVPx9BxLEr+CclA9Wr6aNbu6b4tr8nJP/OHvgnJQOL1x/Euct3kZH1Ek0a2OD/ArrBqkb1Iv0JgoDvpqzHmUuxWDrNF21bNvjQp0QKwFnJslVoxtDPzw/dunUrsj4qKgoSiUQMBPv06YM7d+6U+3jCwsIgkUiKLJqamuV+7MomLCwMhoaGFT0MAmBf2xy3f/1eXH5dN1rc9t30nxD311Ns+eFbnN36f+jcxhkDJ23A9dgHYhu3pvWwcc4g/LEzGJvm+SPh4T/wDVpfEadCJJf1O05ge8R5/F9Ad+xfOx5jBnfChl9OInzfWQCvA72RIWF4+DgZodP98MuPo2BhagT/iWvw4lVOkf427zktBpT06SqclVzWpbL6JErJWlpaMDEx+SDH0tfXx+PHj6WWv/7664Mcm6g8qKmqwNRYX1yqGeqK2/64fg9D+rijSX1rWNc0xrjB7WGgp4XomP8Cw2H9v0AzJxvUMq+KFo1qY5RvO1z68z5y8/Ir4nSISi361l9o41If7i0cUMOsKrzcGqJl47q4EZsIAPjr739wLSYRU4f3gJOdJWwsTTB1eA9kZ+fi0ImrUn3djv8bm3adwswx/6uIUyH6YD6JwLC47NOsWbNgYmICPT09+Pv7Y+LEiXB2di6y78KFC2Fubo5q1aohICAAubm5Mo8lkUhgZmYmtZiamorbPTw8MGLECEyYMAFVq1aFmZkZpk+fLtXH7du30apVK2hqasLR0RFHjx6FRCKRKmsHBQWhXr160NbWRu3atTF16tQiYyvNOa5btw4ODg7Q1NSEvb09VqxYIW67f/8+JBIJduzYATc3N2hpaaFZs2a4c+cOLl68iKZNm0JXVxcdOnTAs2fP5O539+7daNOmDbS1tdGoUSOcP38ewOuM78CBA5GWliZmXd9+j+jDuffgGRw6/B+cu07DkClheJCULG5r3rA29kReRkpaFgoKCrDryCVkZ+ehVZO6xfaVkpaFnYcvoXlDG1RRU/1Qp0D0XpwdrXAhOg73H77+/9vt+Ee4cvM+3JrZAwBycvMAAOrq/11VpaKigipV1HD1ZoK47uWrHEyYuwWTA7rBuKr+BzwDKg8SBS2V1SdxjeHbwsPDMXv2bKxYsQKurq7Ytm0bFi1aBBsbG6l2J06cgLm5OU6cOIG4uDj06dMHzs7OGDJkSJmOv2nTJowZMwYXLlzA+fPn4efnB1dXV7Rr1w75+fno1q0batWqhQsXLiAjIwNjx44t0oeenh7CwsJgYWGBGzduYMiQIdDT08OECRNKfY7h4eEIDg7G8uXL8dlnn+Hq1asYMmQIdHR04OvrK7abNm0alixZglq1amHQoEHo378/9PT0sHTpUmhra6N3794IDg7GypUr5ep38uTJWLhwIerWrYvJkyejX79+iIuLQ8uWLbFkyRIEBwcjNjYWAKCr+1+Wij6cJvWt8eO0AahjZYon/6Rh3tpf0XHIYpzbNhl6OprYOGcQBv3fBtT2DIKaqgq0NNWxecEQ1LaUvr5q2rK9WLfjFF68ykEzJ2ts+2FoBZ0RUen592mDrBfZ6Oy/AKoqEuQXCBjh1x5fftEYAGBjaQJzE0Ms3fArgkf2hLamOn7afRpP/knDs+QMsZ/5q/fD2dEaX/CawkpBBRKolLEWrFKJQ8MKDwwjIiKKBA35+bJLVMuWLcPgwYMxcOBAAEBwcDCOHDmCzMxMqXZGRkZYvnw5VFVVYW9vj06dOuHYsWMyA8O0tLQi43Fzc8Ovv/53AX/Dhg0xbdo0AEDdunWxfPlyHDt2DO3atUNkZCTi4+MRFRUFMzMzAMDs2bPRrl07qT6nTJki/tna2hrjxo3Dtm3bxMCwNOc4bdo0LFq0CD169AAA2NjY4NatW1i9erVUADdu3Dh4e3sDAEaOHIl+/frh2LFjcHV1BQAMHjwYYWFh79Vvp06dAAAhISGoX78+4uLiYG9vDwMDAzH7WpLs7GxkZ2eLr9PT00tsS++nnWt98c8N6tZA0wbWcOocjL1Hr+Crri0xe1UE0jJeYu+Pw1HVUAeHTl7HwEkbcGjtKNSvU0Pcd8RXnviqiwseJCVj3tpfMXT6ZmxfPJTXW9FH7fCp64g4fgXzJvZHHStT3I5/hHmr9sOkmj66tmuKKmqqWBLsi+AfdsC11zSoqqjg88/qwK2ZPQRBAACcOH8TF6LjsXPFqIo9GaIPpMIDwzZt2oiZqkIXLlzAgAEDStwnNjYWw4YNk1rXvHlzHD9+XGpd/fr1oar6X7nL3NwcN27ckDkePT09XLlyRWqdlpaW1OuGDRtKvTY3N8fTp0/FsVlaWkoFRM2bNy9ynO3btyM0NBTx8fHIzMxEXl4e9PX/K1G86xyzsrIQHx+PwYMHSwW6eXl5MDAwKHG8hWVxJycnqXWF43/ffs3NzQEAT58+hb29fZHzLc6cOXMQEhJSqrakGAZ62qhTywT3HjxDwsNnWLvjFM5tmwwH29d/f071auL81Xis++UUFk/qJ+5XzVAX1Qx1UcfKFPWszdDgy6m4eCMBzRvWrqhTIXqnRWsj4N+nDTp6OAMA6tmY4/HTFKzbdhxd2zUFANSvWxO7Vo5BRtZL5Obmo6qhLvqNCEX9ejUBABei4/Dg8XO49AiW6nv0zJ/QuIENwhZ890HPicpOEaXgyvxP4goPDHV0dFCnTh2pdQ8fPlRI31WqVJF6LZFIUFBQIHMfFRWVIuNRRL9vOn/+PHx8fBASEgJvb28YGBiIpeLSKswcrl27Fi1atJDa9mYw/PZ4CzM8b68rHH9Z+5XnfZg0aRLGjBkjvk5PT4elpWWp9yf5Zb7IRsLf/6CPcXNx1qXKW3dqVVWVQCgQSuyj4N9MSuH1WUQfq1fZuUWy2ioqKuJn+E16Oq8TAH/9/Qw37z5EoO/rKot/nzbo2UH6/4Xdv12ECd92gcfnjuU0cipXjAxlqvDA8H3Y2dnh4sWL+Prrr8V1Fy9erMAR/cfOzg4PHjzAkydPxOzc22M7d+4crKysMHnyZHHd2zOf33WOpqamsLCwwL179+Dj46Ow8SuqX3V19XdeEqChoQENDY33Pga929Qlu9HezQmW5lXx+Fka5q45CFUVFfT0bgIDPW3UtqyO0XO2YubI7qhqoIODUddx4kIsti1+fQ3hpT/v48qtv+DSyBYG+tq4//AZZq86CJuaxmjmZPOOoxNVLI/PHbB223GYmxihjpUpYuL/xk+7T6G7VzOxzW+nrsHIQBfmJoa4m/AYc1ftxxcu9eHaxA4AYFxVv9gJJ+YmhqhpVvWDnQvRh/JJBobDhw/HkCFD0LRpU7Rs2RLbt2/H9evXUbt22ctagiAgKSmpyHoTExOoqLx7Ene7du1ga2sLX19fzJ8/HxkZGeL1hIX/cq1bty4SExOxbds2NGvWDAcPHsSePXvkPseQkBCMGDECBgYGaN++PbKzs3Hp0iWkpKRIZeLkpYh+ra2tkZmZiWPHjqFRo0bQ1taGtrb2e4+J3s/fT1PhP2UjktNewNhIFy0a1UbkxrEwNtIDAOxY8h1Clu9DvzGrkfUiGzaW1bFi+lfw+vfaRC3NKog4cQ1z1xzEi5c5MDU2QFsXB4wbNAga6lVkHZqowv3fsG5Ytuk3zFq+G8mpmaheTR//6/g5vvPxFNs8S87A/NUH8Dw1E9Wr6qGLZxMM7e8po1f61PEG17J9koGhj48P7t27h3HjxuHVq1fo3bs3/Pz88Mcff5S57/T0dPF6uTc9fvxY5kSKQqqqqti7dy/8/f3RrFkz1K5dGwsWLEDnzp3FG2V36dIFo0ePRmBgILKzs9GpUydMnTpV6pYupTlHf39/aGtrY8GCBRg/fjx0dHTg5OSEUaNGlek9UES/LVu2xNChQ9GnTx88f/4c06ZN4y1rKsCG7wfJ3G5bywQ/zS95Mlb9OjWwf+UIRQ+L6IPQ0dbExO+6YuJ3XUtsM6BbKwzo1kqufv/8bUFZh0YVSRE3qK68cSEkglDMxRafoHbt2sHMzAybN2+u6KEUcfbsWbRq1QpxcXGwtbV9734+5nNUhPT0dBgYGODJ8zSpiThEyuLv5JcVPQSiCpORkY7P6pghLa18fgMKf2OORSdCV69s/WdmpKOtc61yG2tF+iQzhi9evMCqVavg7e0NVVVVbN26FUePHkVkZGRFDw0AsGfPHujq6qJu3bqIi4vDyJEj4erqKldQ+LGfIxER0aeIc09k+yQDQ4lEgkOHDmH27Nl49eoV7OzssGvXLnh6fhzXhWRkZCAoKAiJiYkwNjaGp6enXDOOgY//HImIiD5JjAxlqjSlZPr0sZRMyo6lZFJmH6qUfPyaYkrJXzRiKZmIiIjok8ZZybIxMCQiIiKlIVHArOTK/DTQd9+Yj4iIiIiUAjOGREREpDQ490Q2BoZERESkPBgZysRSMhEREREBYMaQiIiIlAhnJcvGwJCIiIiUBmcly8ZSMhEREREBYMaQiIiIlAjnnsjGwJCIiIiUByNDmVhKJiIiIiIAzBgSERGREuGsZNmYMSQiIiKlUTgruayLPE6dOoXOnTvDwsICEokEe/fuldouCAKCg4Nhbm4OLS0teHp64u7du1JtkpOT4ePjA319fRgaGmLw4MHIzMyUanP9+nW4ublBU1MTlpaWmD9/vtzvDwNDIiIionKUlZWFRo0a4ccffyx2+/z58xEaGopVq1bhwoUL0NHRgbe3N169eiW28fHxwc2bNxEZGYmIiAicOnUK33zzjbg9PT0dXl5esLKywuXLl7FgwQJMnz4da9askWusLCUTERGR0qiIuScdOnRAhw4dit0mCAKWLFmCKVOmoGvXrgCAn376Caampti7dy/69u2LmJgYHD58GBcvXkTTpk0BAMuWLUPHjh2xcOFCWFhYIDw8HDk5OdiwYQPU1dVRv359REdH44cffpAKIN+FGUMiIiJSHhIFLQqSkJCApKQkeHp6iusMDAzQokULnD9/HgBw/vx5GBoaikEhAHh6ekJFRQUXLlwQ27Ru3Rrq6upiG29vb8TGxiIlJaXU42HGkIiIiOg9pKenS73W0NCAhoaGXH0kJSUBAExNTaXWm5qaituSkpJgYmIitV1NTQ1Vq1aVamNjY1Okj8JtRkZGpRoPM4ZERESkNCQK+g8ALC0tYWBgIC5z5syp4LMrO2YMiYiISGko8lnJDx48gL6+vrhe3mwhAJiZmQEAnjx5AnNzc3H9kydP4OzsLLZ5+vSp1H55eXlITk4W9zczM8OTJ0+k2hS+LmxTGswYEhEREb0HfX19qeV9AkMbGxuYmZnh2LFj4rr09HRcuHABLi4uAAAXFxekpqbi8uXLYpvjx4+joKAALVq0ENucOnUKubm5YpvIyEjY2dmVuowMMDAkIiIiJVIRc08yMzMRHR2N6OhoAK8nnERHRyMxMRESiQSjRo3CrFmzsH//fty4cQNff/01LCws0K1bNwCAg4MD2rdvjyFDhuCPP/7A2bNnERgYiL59+8LCwgIA0L9/f6irq2Pw4MG4efMmtm/fjqVLl2LMmDFyjZWlZCIiIlIeFXC/mkuXLqFNmzbi68JgzdfXF2FhYZgwYQKysrLwzTffIDU1Fa1atcLhw4ehqakp7hMeHo7AwEC0bdsWKioq6NmzJ0JDQ8XtBgYGOHLkCAICAtCkSRMYGxsjODhYrlvVAIBEEARBvtMjKh/p6ekwMDDAk+dpUtdsECmLv5NfVvQQiCpMRkY6PqtjhrS08vkNKPyN+SP2EXT1ytZ/ZkY6mttZlNtYKxIzhkRERKQ0+Kxk2RgYEhERkdJQ5KzkyoiTT4iIiIgIADOGREREpEQq4lnJnxIGhkRERKQ8GBnKxFIyEREREQFgxpCIiIiUCGcly8bAkIiIiJSHAmYlV+K4kKVkIiIiInqNGUMiIiJSGpx7IhsDQyIiIlIejAxlYimZiIiIiAAwY0hERERKhLOSZWNgSEREREqDz0qWjaVkIiIiIgLAjCEREREpEc49kY2BIRERESkPRoYysZRMRERERACYMSQiIiIlwlnJsjEwJCIiIqUhgQJmJStkJB8nlpKJiIiICAAzhkRERKREOPdENgaGREREpDR4g2vZWEomIiIiIgDMGBIREZFSYTFZFgaGREREpDRYSpaNpWQiIiIiAsCMIRERESkRFpJlY2BIRERESoOlZNlYSiYiIiIiAMwYEhERkRLhs5JlY2BIREREyoMXGcrEUjIRERERAWDGkIiIiJQIE4ayMTAkIiIipcFZybKxlExEREREAJgxJCIiIiXCWcmyMTAkIiIi5cGLDGViKZmIiIiIADBjSEREREqECUPZGBgSERGR0uCsZNlYSiYiIiIiAMwYEhERkVIp+6zkylxMZmBIRERESoOlZNlYSiYiIiIiAAwMiYiIiOhfLCUTERGR0mApWTZmDImIiIgIADOGREREpET4rGTZGBgSERGR0mApWTaWkomIiIgIADOGREREpET4rGTZGBgSERGR8mBkKBNLyUREREQEgBlDIiIiUiKclSwbA0MiIiJSGpyVLBtLyUREREQEgBlDIiIiUiKceyIbA0MiIiJSHowMZWIpmYiIiIgAMGNIRERESoSzkmVjYEhERERKg7OSZWNgSB8NQRAAABnp6RU8EqKKkZHxsqKHQFRhMjMyAPz3W1Be0hXwG6OIPj5WDAzpo5Hx7/8U6thYVvBIiIioomRkZMDAwEDh/aqrq8PMzAx1FfQbY2ZmBnV1dYX09TGRCOUdmhOVUkFBAR49egQ9PT1IKnOe/iOVnp4OS0tLPHjwAPr6+hU9HKIPip//iicIAjIyMmBhYQEVlfKZG/vq1Svk5OQopC91dXVoamoqpK+PCTOG9NFQUVFBzZo1K3oYSk9fX58/jKS0+PmvWOWRKXyTpqZmpQzmFIm3qyEiIiIiAAwMiYiIiOhfDAyJCACgoaGBadOmQUNDo6KHQvTB8fNP9BonnxARERERAGYMiYiIiOhfDAyJiIiICAADQyIiKoOwsDAYGhrKtY+fnx+6detWLuMhorJhYEhEiIqKgkQiQWpqakUPhT4SJQVvb39W+vTpgzt37pT7eMLCwiCRSIosvCed/N4nmCflwcCQSMH8/PwgkUgwd+5cqfV79+795J7ocvLkSVhavn58FLM8VBwtLS2YmJh8kGPp6+vj8ePHUstff/31QY5NpCwYGBKVA01NTcybNw8pKSkK61NRj3GSx759+9C5c+cPftz3URHvDxWffZo1axZMTEygp6cHf39/TJw4Ec7OzkX2XbhwIczNzVGtWjUEBAQgNzdX5rEkEgnMzMykFlNTU3G7h4cHRowYgQkTJqBq1aowMzPD9OnTpfq4ffs2WrVqBU1NTTg6OuLo0aOQSCTYu3ev2CYoKAj16tWDtrY2ateujalTpxYZW2nOcd26dXBwcICmpibs7e2xYsUKcdv9+/chkUiwY8cOuLm5QUtLC82aNcOdO3dw8eJFNG3aFLq6uujQoQOePXsmd7+7d+9GmzZtoK2tjUaNGuH8+fMAXmd8Bw4ciLS0NDHr+vZ7RMqNgSFROfD09ISZmRnmzJlTYptdu3ahfv360NDQgLW1NRYtWiS13draGjNnzsTXX38NfX19fPPNN+KPcEREBOzs7KCtrY1evXrhxYsX2LRpE6ytrWFkZIQRI0YgPz9f7Gvz5s1o2rQp9PT0YGZmhv79++Pp06fvPI/9+/ejS5cupTrnkydPonnz5tDQ0IC5uTkmTpyIvLw8AEBERAQMDQ3FMUVHR0MikWDixIni/v7+/hgwYID4+syZM+IPpqWlJUaMGIGsrCyZ7w9VvPDwcMyePRvz5s3D5cuXUatWLaxcubJIuxMnTiA+Ph4nTpzApk2bEBYWhrCwsDIff9OmTdDR0cGFCxcwf/58zJgxA5GRkQCA/Px8dOvWDdra2rhw4QLWrFmDyZMnF+lDT08PYWFhuHXrFpYuXYq1a9di8eLFcp1jeHg4goODMXv2bMTExOD777/H1KlTsWnTJql206ZNw5QpU3DlyhWoqamhf//+mDBhApYuXYrTp08jLi4OwcHBcvc7efJkjBs3DtHR0ahXrx769euHvLw8tGzZEkuWLJHKvo4bN67M7ztVIgIRKZSvr6/QtWtXYffu3YKmpqbw4MEDQRAEYc+ePULhV+7SpUuCioqKMGPGDCE2NlbYuHGjoKWlJWzcuFHsx8rKStDX1xcWLlwoxMXFCXFxccLGjRuFKlWqCO3atROuXLkinDx5UqhWrZrg5eUl9O7dW7h586Zw4MABQV1dXdi2bZvY1/r164VDhw4J8fHxwvnz5wUXFxehQ4cO4vYTJ04IAISUlBRx3Z9//ino6ekJ2dnZUudVnIcPHwra2trCsGHDhJiYGGHPnj2CsbGxMG3aNEEQBCE1NVVQUVERLl68KAiCICxZskQwNjYWWrRoIfZRp04dYe3atYIgCEJcXJygo6MjLF68WLhz545w9uxZ4bPPPhP8/Pxkvj+kOL6+voKqqqqgo6MjtWhqakp9VjZu3CgYGBiI+7Vo0UIICAiQ6svV1VVo1KiRVN9WVlZCXl6euO5///uf0KdPnxLHs3HjRgFAkfG0b99ebOPu7i60atVKar9mzZoJQUFBgiAIwq+//iqoqakJjx8/FrdHRkYKAIQ9e/aUeOwFCxYITZo0kescbW1thS1btki1mTlzpuDi4iIIgiAkJCQIAIR169aJ27du3SoAEI4dOyaumzNnjmBnZ1emfm/evCkAEGJiYgRBKPp3RvQmtQqLSIkque7du8PZ2RnTpk3D+vXrpbb98MMPaNu2LaZOnQoAqFevHm7duoUFCxbAz89PbPfFF19g7Nix4uvTp08jNzcXK1euhK2tLQCgV69e2Lx5M548eQJdXV04OjqiTZs2OHHiBPr06QMAGDRokNhH7dq1ERoaimbNmiEzMxO6urrFjn/fvn3w9vaGurr6O891xYoVsLS0xPLlyyGRSGBvb49Hjx4hKCgIwcHBMDAwgLOzM6KiotC0aVNERUVh9OjRCAkJQWZmJtLS0hAXFwd3d3cAwJw5c+Dj44NRo0YBAOrWrYvQ0FC4u7tj5cqV4oSDt98fUqw2bdoUyYRduHBBKrP7ttjYWAwbNkxqXfPmzXH8+HGpdfXr14eqqqr42tzcHDdu3JA5Hj09PVy5ckVqnZaWltTrhg0bSr02NzcXs+OxsbGwtLSEmZmZ1Njetn37doSGhiI+Ph6ZmZnIy8uDvr5+qc8xKysL8fHxGDx4MIYMGSK2ycvLg4GBQYnjLSyLOzk5Sa0rHP/79mtubg4AePr0Kezt7YucL9GbGBgSlaN58+bhiy++KFKqiYmJQdeuXaXWubq6YsmSJcjPzxd/MJs2bVqkT21tbTEoBF7/cFhbW0sFeG/+mADA5cuXMX36dFy7dg0pKSkoKCgAACQmJsLR0bHYse/btw+BgYGlOs+YmBi4uLhITa5xdXVFZmYmHj58iFq1asHd3R1RUVEYO3YsTp8+jTlz5mDHjh04c+YMkpOTYWFhgbp16wIArl27huvXryM8PFzsTxAEFBQUICEhAQ4ODiW+P6Q4Ojo6qFOnjtS6hw8fKqTvKlWqSL2WSCTi57IkKioqRcajiH7fdP78efj4+CAkJATe3t4wMDDAtm3bilzqIUtmZiYAYO3atWjRooXUtjeD4bfHW/j9eXtd4fjL2q887wMpLwaGROWodevW8Pb2xqRJk6QygaWlo6NTZF1xP3yyfgyzsrLg7e0Nb29vhIeHo3r16khMTIS3t3eJEzYeP36Mq1evolOnTnKPuSQeHh7YsGEDrl27hipVqsDe3h4eHh6IiopCSkqKmC0EXv8AfvvttxgxYkSRfmrVqiX+ubj3hyqWnZ0dLl68iK+//lpcd/HixQoc0X/s7Ozw4MEDPHnyRMzOvT22c+fOwcrKSuraw7dnPr/rHE1NTWFhYYF79+7Bx8dHYeNXVL/q6upS1yATvYmBIVE5mzt3LpydnWFnZyeuc3BwwNmzZ6XanT17FvXq1SvyL/+yun37Np4/f465c+eKt565dOmSzH0OHDiAli1bomrVqqU6hoODA3bt2gVBEMTsxNmzZ6Gnp4eaNWsCANzc3JCRkYHFixeLQaCHhwfmzp2LlJQUqZJw48aNcevWrXdmh+jjM3z4cAwZMgRNmzZFy5YtsX37dly/fh21a9cuc9+CICApKanIehMTE6iovHsuZbt27WBrawtfX1/Mnz8fGRkZmDJlCoD/smp169ZFYmIitm3bhmbNmuHgwYPYs2eP3OcYEhKCESNGwMDAAO3bt0d2djYuXbqElJQUjBkz5r3fA0X0a21tjczMTBw7dgyNGjWCtrY2tLW133tMVLlwVjJROXNycoKPjw9CQ0PFdWPHjsWxY8cwc+ZM3LlzB5s2bcLy5cvLZXZgrVq1oK6ujmXLluHevXvYv38/Zs6cKXOfkmYjp6WlITo6Wmp58OABhg0bhgcPHmD48OG4ffs29u3bh2nTpmHMmDHiD7aRkREaNmyI8PBweHh4AHidUb1y5Qru3LkjlTEMCgrCuXPnEBgYiOjoaNy9e1eu0jZVHB8fH0yaNAnjxo1D48aNkZCQAD8/P4XciDo9PR3m5uZFltLMsAdel1v37t2LzMxMNGvWDP7+/mJmsHB8Xbp0wejRoxEYGAhnZ2ecO3dOvBZYnnP09/fHunXrsHHjRjg5OcHd3R1hYWGwsbEp03ugiH5btmyJoUOHok+fPqhevTrmz59fpjFRJVPBk1+IKp3iZu8mJCQI6urqwptfuZ07dwqOjo5ClSpVhFq1agkLFiyQ2sfKykpYvHix1LriZhNOmzZNajZkcWPYsmWLYG1tLWhoaAguLi7C/v37BQDC1atXBUGQnpWcmZkpaGpqCnfv3i3SJ4Aiy+DBgwVBEISoqCihWbNmgrq6umBmZiYEBQUJubm5Un2MHDlSanakIAhCo0aNBDMzsyLv4x9//CG0a9dO0NXVFXR0dISGDRsKs2fPlvn+0MfJ09NTGDBgQEUPo1hnzpwRAJR5VvvHfI5E8pAIgiBUVFBKRB+f3bt3Y8qUKbh161ZFD4U+QS9evMCqVavg7e0NVVVVbN26VbyXoKenZ0UPD3v27IGuri7q1q2LuLg4jBw5EkZGRjhz5kyp+/jYz5GoLHiNIRFJ0dXVxbx58yp6GPSJkkgkOHToEGbPno1Xr17Bzs4Ou3bt+mgCpoyMDAQFBSExMRHGxsbw9PSUa8Yx8PGfI1FZMGNIRERERAA4+YSIiIiI/sXAkIiIiIgAMDAkIiIion8xMCQiIiIiAAwMiYiIiOhfDAyJiOTg5+eHbt26ia89PDwwatSoDz6OqKgoSCQSpKamlthGIpFg7969pe5z+vTpcHZ2LtO47t+/D4lEgujo6DL1Q0QVg4EhEX3y/Pz8IJFIIJFIoK6ujjp16mDGjBnIy8sr92Pv3r37nY8YLFSaYI6IqCLxBtdEVCm0b98eGzduRHZ2Ng4dOoSAgABUqVIFkyZNKtI2JycH6urqCjlu1apVFdIPEdHHgBlDIqoUNDQ0YGZmBisrK3z33Xfw9PTE/v37AfxX/p09ezYsLCxgZ2cHAHjw4AF69+4NQ0NDVK1aFV27dsX9+/fFPvPz8zFmzBgYGhqiWrVqmDBhAt5+JsDbpeTs7GwEBQXB0tISGhoaqFOnDtavX4/79++jTZs2AAAjIyNIJBL4+fkBAAoKCjBnzhzY2NhAS0sLjRo1ws6dO6WOc+jQIdSrVw9aWlpo06aN1DhLKygoCPXq1YO2tjZq166NqVOnIjc3t0i71atXw9LSEtra2ujduzfS0tKktq9btw4ODg7Q1NSEvb09VqxYIfdYiOjjxMCQiColLS0t5OTkiK+PHTuG2NhYREZGIiIiArm5ufD29oaenh5Onz6Ns2fPQldXF+3btxf3W7RoEcLCwrBhwwacOXMGycnJ2LNnj8zjfv3119i6dStCQ0MRExOD1atXQ1dXF5aWlti1axcAIDY2Fo8fP8bSpUsBAHPmzMFPP/2EVatW4ebNmxg9ejQGDBiAkydPAngdwPbo0QOdO3dGdHQ0/P39MXHiRLnfEz09PYSFheHWrVtYunQp1q5di8WLF0u1iYuLw44dO3DgwAEcPnwYV69exbBhw8Tt4eHhCA4OxuzZsxETE4Pvv/8eU6dOxaZNm+QeDxF9hAQiok+cr6+v0LVrV0EQBKGgoECIjIwUNDQ0hHHjxonbTU1NhezsbHGfzZs3C3Z2dkJBQYG4Ljs7W9DS0hJ+++03QRAEwdzcXJg/f764PTc3V6hZs6Z4LEEQBHd3d2HkyJGCIAhCbGysAECIjIwsdpwnTpwQAAgpKSniulevXgna2trCuXPnpNoOHjxY6NevnyAIgjBp0iTB0dFRantQUFCRvt4GQNizZ0+J2xcsWCA0adJEfD1t2jRBVVVVePjwobju119/FVRUVITHjx8LgiAItra2wpYtW6T6mTlzpuDi4iIIgiAkJCQIAISrV6+WeFwi+njxGkMiqhQiIiKgq6uL3NxcFBQUoH///pg+fbq43cnJSeq6wmvXriEuLg56enpS/bx69Qrx8fFIS0vD48eP0aJFC3GbmpoamjZtWqScXCg6Ohqqqqpwd3cv9bjj4uLw4sULtGvXTmp9Tk4OPvvsMwBATEyM1DgAwMXFpdTHKLR9+3aEhoYiPj4emZmZyMvLg76+vlSbWrVqoUaNGlLHKSgoQGxsLPT09BAfH4/BgwdjyJAhYpu8vDwYGBjIPR4i+vgwMCSiSqFNmzZYuXIl1NXVYWFhATU16f+96ejoSL3OzMxEkyZNEB4eXqSv6tWrv9cYtLS05N4nMzMTAHDw4EGpgAx4fd2kopw/fx4+Pj4ICQmBt7c3DAwMsG3bNixatEjusa5du7ZIoKqqqqqwsRJRxWFgSESVgo6ODurUqVPq9o0bN8b27dthYmJSJGtWyNzcHBcuXEDr1q0BvM6MXb58GY0bNy62vZOTEwoKCnDy5El4enoW2V6YsczPzxfXOTo6QkNDA4mJiSVmGh0cHMSJNIV+//33d5/kG86dOwcrKytMnjxZXPfXX38VaZeYmIhHjx7BwsJCPI6Kigrs7OxgamoKCwsL3Lt3Dz4+PnIdn4g+DZx8QkRKycfHB8bGxujatStOnz6NhIQEREVFYcSIEXj48CEAYOTIkZg7dy727t2L27dvY9iwYTLvQWhtbQ1fX18MGjQIe/fuFfvcsWMHAMDKygoSiQQRERF49uwZMjMzoaenh3HjxmH06NHYtGkT4uPjceXKFSxbtkyc0DF06FDcvXsX48ePR2xsLLZs2YKwsDC5zrdu3bpITEzEtm3bEB8fj9DQ0GIn0mhqasLX1xfXrl3D6dOnMWLECPTu3RtmZmYAgJCQEMyZMwehoaG4c+cObty4gY0bN+KHH36QazxE9HFiYEhESklbWxunTp1CrVq10KNHDzg4OGDw4MF49eqVmEEcO3YsvvrqK/j6+sLFxQV6enro3r27zH5XrlyJXr16YdiwYbC3t8eQIUOQlZUFAKhRowZCQkIwceJEmJqaIjAwEAAwc+ZMTJ06FXPmzIGDgwPat2+PgwcPwsbGBsDr6/527dqFvXv3olGjRli1ahW+//57uc63S5cuGD16NAIDA+Hs7Ixz585h6tSpRdrVqVMHPXr0QMeOHeHl5YWGDRtK3Y7G398f69atw8aNG+Hk5AR3d3eEhYWJYyWiT5tEKOkqaiIiIiJSKswYEhEREREABoZERERE9C8GhkREREQEgIEhEREREf2LgSERERERAWBgSERERET/YmBIRERERAAYGBIRERHRvxgYEhEREREABoZERERE9C8GhkREREQEgIEhEREREf3r/wFuPSPhUzSIAQAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Decision Tree — Classification Report\n",
            "                 precision    recall  f1-score   support\n",
            "\n",
            "   Normal/Lower       0.84      0.59      0.70      4438\n",
            "High Engagement       0.35      0.66      0.46      1477\n",
            "\n",
            "       accuracy                           0.61      5915\n",
            "      macro avg       0.60      0.63      0.58      5915\n",
            "   weighted avg       0.72      0.61      0.64      5915\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Random Forest — Classification Report\n",
            "                 precision    recall  f1-score   support\n",
            "\n",
            "   Normal/Lower       0.82      0.72      0.77      4438\n",
            "High Engagement       0.39      0.53      0.45      1477\n",
            "\n",
            "       accuracy                           0.67      5915\n",
            "      macro avg       0.60      0.63      0.61      5915\n",
            "   weighted avg       0.71      0.67      0.69      5915\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# 8.3 — Evaluate classification models\n",
        "\n",
        "\n",
        "classification_results = []\n",
        "\n",
        "for name, model in trained_classifiers.items():\n",
        "    preds = model.predict(X_test_clf)\n",
        "\n",
        "    print(f\"\\n{name} — Classification Report\")\n",
        "\n",
        "    report = classification_report(\n",
        "        y_test_clf,\n",
        "        preds,\n",
        "        labels=[0, 1],\n",
        "        target_names=[\"Normal/Lower\", \"High Engagement\"],\n",
        "        output_dict=True,\n",
        "        zero_division=0\n",
        "    )\n",
        "\n",
        "    print(classification_report(\n",
        "        y_test_clf,\n",
        "        preds,\n",
        "        labels=[0, 1],\n",
        "        target_names=[\"Normal/Lower\", \"High Engagement\"],\n",
        "        zero_division=0\n",
        "    ))\n",
        "\n",
        "    # Store key metrics\n",
        "    classification_results.append({\n",
        "        \"model\": name,\n",
        "        \"accuracy\": report[\"accuracy\"],\n",
        "        \"precision_class1\": report[\"High Engagement\"][\"precision\"],\n",
        "        \"recall_class1\": report[\"High Engagement\"][\"recall\"],\n",
        "        \"f1_class1\": report[\"High Engagement\"][\"f1-score\"],\n",
        "        \"support_class1\": report[\"High Engagement\"][\"support\"],\n",
        "        \"macro_f1\": report[\"macro avg\"][\"f1-score\"]\n",
        "    })\n",
        "\n",
        "    # Confusion matrix\n",
        "    ConfusionMatrixDisplay.from_predictions(\n",
        "        y_test_clf,\n",
        "        preds,\n",
        "        labels=[0, 1],\n",
        "        display_labels=[\"Normal/Lower\", \"High Engagement\"],\n",
        "        cmap=\"Blues\"\n",
        "    )\n",
        "\n",
        "    plt.title(f\"{name} — Confusion Matrix\")\n",
        "    plt.tight_layout()\n",
        "    plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Classification Model Evaluation\n",
        "\n",
        "The classification models were evaluated using precision, recall, F1-score, and confusion matrices because the target is imbalanced: about 75% of postings are normal/lower engagement and about 25% are high engagement.\n",
        "\n",
        "The Dummy Classifier showed that accuracy alone is misleading, since a model can achieve approximately 75% accuracy by predicting only the majority class while completely failing to identify high-engagement postings.\n",
        "\n",
        "Among the real models, Decision Tree was selected as the strongest model for the High Engagement class based on F1-score and recall. It achieved the highest Class 1 F1-score and the highest Class 1 recall, meaning it detected the largest number of truly high-engagement postings.\n",
        "\n",
        "Logistic Regression performed almost identically to Decision Tree in terms of F1-score, but Decision Tree was slightly stronger in the final table. Random Forest achieved the highest overall accuracy and highest Class 1 precision, but its recall was lower, meaning it missed more truly high-engagement postings.\n",
        "\n",
        "The confusion matrices show the main tradeoff: Decision Tree catches the most high-engagement postings, but it also creates the most false positives. Random Forest is more conservative and creates fewer false positives, but it misses more actual high-engagement postings.\n",
        "\n",
        "Overall, Decision Tree is selected as the strongest classification model based on Class 1 F1-score, while Random Forest may be preferred if the business priority is minimizing false alarms."
      ],
      "metadata": {
        "id": "sHIC3Pz3_OiA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 8.4 — Compare classification models\n",
        "\n",
        "classification_results_df = pd.DataFrame(classification_results)\n",
        "\n",
        "classification_results_df = classification_results_df.sort_values(\n",
        "    by=[\"f1_class1\", \"recall_class1\"],\n",
        "    ascending=False\n",
        ")\n",
        "\n",
        "display(classification_results_df.round(3))"
      ],
      "metadata": {
        "id": "vcNH_0-Ht-p1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 144
        },
        "outputId": "eda72306-9d2b-463b-900b-1bbfc2d658cc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                 model  accuracy  precision_class1  recall_class1  f1_class1  \\\n",
              "1        Decision Tree     0.612             0.353          0.664      0.460   \n",
              "0  Logistic Regression     0.644             0.370          0.605      0.459   \n",
              "2        Random Forest     0.675             0.389          0.527      0.447   \n",
              "\n",
              "   support_class1  macro_f1  \n",
              "1          1477.0     0.579  \n",
              "0          1477.0     0.597  \n",
              "2          1477.0     0.608  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-1733db10-c0fe-4d55-8927-6d99e7d761e7\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>model</th>\n",
              "      <th>accuracy</th>\n",
              "      <th>precision_class1</th>\n",
              "      <th>recall_class1</th>\n",
              "      <th>f1_class1</th>\n",
              "      <th>support_class1</th>\n",
              "      <th>macro_f1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Decision Tree</td>\n",
              "      <td>0.612</td>\n",
              "      <td>0.353</td>\n",
              "      <td>0.664</td>\n",
              "      <td>0.460</td>\n",
              "      <td>1477.0</td>\n",
              "      <td>0.579</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Logistic Regression</td>\n",
              "      <td>0.644</td>\n",
              "      <td>0.370</td>\n",
              "      <td>0.605</td>\n",
              "      <td>0.459</td>\n",
              "      <td>1477.0</td>\n",
              "      <td>0.597</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Random Forest</td>\n",
              "      <td>0.675</td>\n",
              "      <td>0.389</td>\n",
              "      <td>0.527</td>\n",
              "      <td>0.447</td>\n",
              "      <td>1477.0</td>\n",
              "      <td>0.608</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1733db10-c0fe-4d55-8927-6d99e7d761e7')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-1733db10-c0fe-4d55-8927-6d99e7d761e7 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-1733db10-c0fe-4d55-8927-6d99e7d761e7');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"display(classification_results_df\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"model\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Decision Tree\",\n          \"Logistic Regression\",\n          \"Random Forest\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"accuracy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.03150132272355139,\n        \"min\": 0.612,\n        \"max\": 0.675,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.612,\n          0.644,\n          0.675\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"precision_class1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.018009256878986815,\n        \"min\": 0.353,\n        \"max\": 0.389,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.353,\n          0.37,\n          0.389\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"recall_class1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.06871923554095559,\n        \"min\": 0.527,\n        \"max\": 0.664,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.664,\n          0.605,\n          0.527\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"f1_class1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.007234178138070242,\n        \"min\": 0.447,\n        \"max\": 0.46,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.46,\n          0.459,\n          0.447\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"support_class1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 1477.0,\n        \"max\": 1477.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1477.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"macro_f1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.014640127503998512,\n        \"min\": 0.579,\n        \"max\": 0.608,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.579\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 8.5 — Compare Class 1 precision, recall, and F1-score\n",
        "\n",
        "classification_metrics_long = classification_results_df.melt(\n",
        "    id_vars=\"model\",\n",
        "    value_vars=[\"precision_class1\", \"recall_class1\", \"f1_class1\"],\n",
        "    var_name=\"metric\",\n",
        "    value_name=\"score\"\n",
        ")\n",
        "\n",
        "metric_name_map = {\n",
        "    \"precision_class1\": \"Precision — High Engagement\",\n",
        "    \"recall_class1\": \"Recall — High Engagement\",\n",
        "    \"f1_class1\": \"F1-score — High Engagement\"\n",
        "}\n",
        "\n",
        "classification_metrics_long[\"metric\"] = classification_metrics_long[\"metric\"].map(metric_name_map)\n",
        "\n",
        "plt.figure(figsize=(10, 6))\n",
        "\n",
        "sns.barplot(\n",
        "    data=classification_metrics_long,\n",
        "    x=\"score\",\n",
        "    y=\"model\",\n",
        "    hue=\"metric\"\n",
        ")\n",
        "\n",
        "plt.title(\"Classification Model Comparison — High Engagement Class\")\n",
        "plt.xlabel(\"Score\")\n",
        "plt.ylabel(\"Model\")\n",
        "\n",
        "plt.xlim(0, 1)\n",
        "\n",
        "plt.legend(title=\"Metric\", loc=\"lower right\")\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "id": "1gHLj6Nn_kPz",
        "outputId": "db5c892a-3568-48c5-f5f6-9be664069f45"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Classification Model Comparison — High Engagement Class\n",
        "\n",
        "The classification comparison focuses on the High Engagement class because this is the business-relevant class. Since only about 25% of postings are labeled as high engagement, accuracy alone is not enough to evaluate model quality.\n",
        "\n",
        "Decision Tree achieved the highest F1-score for the High Engagement class and also had the highest recall. This means it was the best model for detecting truly high-engagement postings. It correctly identified more high-engagement jobs than Logistic Regression and Random Forest, although it also produced more false positives.\n",
        "\n",
        "Random Forest achieved the highest precision and highest overall accuracy. This means that, compared with the other models, when Random Forest predicted a posting as high engagement, it was more likely to be correct. However, its recall was lower, meaning it missed more truly high-engagement postings.\n",
        "\n",
        "Logistic Regression performed very close to Decision Tree in terms of F1-score, but Decision Tree was slightly stronger based on the final comparison table.\n",
        "\n",
        "Therefore, the best model depends on the business goal. If the goal is to catch as many high-engagement postings as possible, Decision Tree is preferred because of its higher recall. If the goal is to reduce false positives, Random Forest may be preferred because of its higher precision. Based on F1-score, which balances precision and recall, Decision Tree is the strongest overall classifier for the High Engagement class, although its advantage over Logistic Regression is very small."
      ],
      "metadata": {
        "id": "ObKDFC5nuv0n"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The confusion matrices show the tradeoff between the models. Decision Tree catches the most actual high-engagement postings and has the lowest false-negative count, but it also creates the most false positives. Random Forest is more conservative, producing fewer false positives, but it misses more actual high-engagement postings. Logistic Regression performs close to Decision Tree in F1-score, but Decision Tree is slightly stronger in the final comparison table.\n",
        "\n",
        "Because both types of mistakes matter, the main model-selection metric is F1-score for the High Engagement class. F1-score balances precision and recall, while recall is still examined carefully because missing high-engagement postings is costly.\n",
        "\n",
        "- False Negative = missed high-engagement posting\n",
        "- False Positive = mistakenly flagged normal/lower-engagement posting as high engagement"
      ],
      "metadata": {
        "id": "NacF_g35_KJy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "classification_results_df = classification_results_df.sort_values(\n",
        "    by=[\"f1_class1\", \"recall_class1\"],\n",
        "    ascending=False\n",
        ")\n",
        "\n",
        "classification_results_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 144
        },
        "id": "veVWbTDfHimV",
        "outputId": "062a19ed-f44c-4b00-ad9c-30fcb23e9d3b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 model  accuracy  precision_class1  recall_class1  f1_class1  \\\n",
              "1        Decision Tree  0.611665          0.352518       0.663507   0.460418   \n",
              "0  Logistic Regression  0.643956          0.369880       0.605281   0.459168   \n",
              "2        Random Forest  0.674894          0.388611       0.526743   0.447255   \n",
              "\n",
              "   support_class1  macro_f1  \n",
              "1          1477.0  0.578552  \n",
              "0          1477.0  0.596897  \n",
              "2          1477.0  0.608492  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ccc4168e-bed9-4212-b879-1f1944d7fcf7\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>model</th>\n",
              "      <th>accuracy</th>\n",
              "      <th>precision_class1</th>\n",
              "      <th>recall_class1</th>\n",
              "      <th>f1_class1</th>\n",
              "      <th>support_class1</th>\n",
              "      <th>macro_f1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Decision Tree</td>\n",
              "      <td>0.611665</td>\n",
              "      <td>0.352518</td>\n",
              "      <td>0.663507</td>\n",
              "      <td>0.460418</td>\n",
              "      <td>1477.0</td>\n",
              "      <td>0.578552</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Logistic Regression</td>\n",
              "      <td>0.643956</td>\n",
              "      <td>0.369880</td>\n",
              "      <td>0.605281</td>\n",
              "      <td>0.459168</td>\n",
              "      <td>1477.0</td>\n",
              "      <td>0.596897</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Random Forest</td>\n",
              "      <td>0.674894</td>\n",
              "      <td>0.388611</td>\n",
              "      <td>0.526743</td>\n",
              "      <td>0.447255</td>\n",
              "      <td>1477.0</td>\n",
              "      <td>0.608492</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ccc4168e-bed9-4212-b879-1f1944d7fcf7')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-ccc4168e-bed9-4212-b879-1f1944d7fcf7 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-ccc4168e-bed9-4212-b879-1f1944d7fcf7');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "  <div id=\"id_11b3cc0a-1128-4505-a2bf-7c69cafe78b8\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('classification_results_df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_11b3cc0a-1128-4505-a2bf-7c69cafe78b8 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('classification_results_df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "classification_results_df",
              "summary": "{\n  \"name\": \"classification_results_df\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"model\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Decision Tree\",\n          \"Logistic Regression\",\n          \"Random Forest\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"accuracy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.031616950077069585,\n        \"min\": 0.611665257819104,\n        \"max\": 0.674894336432798,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.611665257819104,\n          0.643956043956044,\n          0.674894336432798\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"precision_class1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.018051030246333807,\n        \"min\": 0.35251798561151076,\n        \"max\": 0.3886113886113886,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.35251798561151076,\n          0.36988001654944147,\n          0.3886113886113886\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"recall_class1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0686327739876733,\n        \"min\": 0.5267433987813135,\n        \"max\": 0.6635071090047393,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.6635071090047393,\n          0.6052809749492214,\n          0.5267433987813135\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"f1_class1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.00726580534218562,\n        \"min\": 0.4472549583213567,\n        \"max\": 0.4604181348367395,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.4604181348367395,\n          0.4591679506933744,\n          0.4472549583213567\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"support_class1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 1477.0,\n        \"max\": 1477.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1477.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"macro_f1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.015096193488288149,\n        \"min\": 0.5785518641963969,\n        \"max\": 0.608491567293836,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.5785518641963969\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 226
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iOW61vwUtEi1"
      },
      "source": [
        "#### 8.4: Winner"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2t9YFU9KtH8p"
      },
      "source": [
        "- Choose the best one out of the three models.\n",
        "\n",
        "- Export the model to a `pickle` file.\n",
        "\n",
        "- Upload the pickle file to the same(!) model repository.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-GsJatjRsvZ-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 161
        },
        "outputId": "608cb4da-ae75-4c22-c43f-d761f335f049"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Winning classification model: Decision Tree\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                 model  accuracy  precision_class1  recall_class1  f1_class1  \\\n",
              "0        Decision Tree     0.612             0.353          0.664      0.460   \n",
              "1  Logistic Regression     0.644             0.370          0.605      0.459   \n",
              "2        Random Forest     0.675             0.389          0.527      0.447   \n",
              "\n",
              "   support_class1  macro_f1  \n",
              "0          1477.0     0.579  \n",
              "1          1477.0     0.597  \n",
              "2          1477.0     0.608  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-80a9f19d-1cf1-4afd-a3fa-8aa860210a6f\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>model</th>\n",
              "      <th>accuracy</th>\n",
              "      <th>precision_class1</th>\n",
              "      <th>recall_class1</th>\n",
              "      <th>f1_class1</th>\n",
              "      <th>support_class1</th>\n",
              "      <th>macro_f1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Decision Tree</td>\n",
              "      <td>0.612</td>\n",
              "      <td>0.353</td>\n",
              "      <td>0.664</td>\n",
              "      <td>0.460</td>\n",
              "      <td>1477.0</td>\n",
              "      <td>0.579</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Logistic Regression</td>\n",
              "      <td>0.644</td>\n",
              "      <td>0.370</td>\n",
              "      <td>0.605</td>\n",
              "      <td>0.459</td>\n",
              "      <td>1477.0</td>\n",
              "      <td>0.597</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Random Forest</td>\n",
              "      <td>0.675</td>\n",
              "      <td>0.389</td>\n",
              "      <td>0.527</td>\n",
              "      <td>0.447</td>\n",
              "      <td>1477.0</td>\n",
              "      <td>0.608</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-80a9f19d-1cf1-4afd-a3fa-8aa860210a6f')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-80a9f19d-1cf1-4afd-a3fa-8aa860210a6f button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-80a9f19d-1cf1-4afd-a3fa-8aa860210a6f');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"ipy_display(classification_results_df\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"model\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Decision Tree\",\n          \"Logistic Regression\",\n          \"Random Forest\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"accuracy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.03150132272355139,\n        \"min\": 0.612,\n        \"max\": 0.675,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.612,\n          0.644,\n          0.675\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"precision_class1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.018009256878986815,\n        \"min\": 0.353,\n        \"max\": 0.389,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.353,\n          0.37,\n          0.389\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"recall_class1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.06871923554095559,\n        \"min\": 0.527,\n        \"max\": 0.664,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.664,\n          0.605,\n          0.527\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"f1_class1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.007234178138070242,\n        \"min\": 0.447,\n        \"max\": 0.46,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.46,\n          0.459,\n          0.447\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"support_class1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 1477.0,\n        \"max\": 1477.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1477.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"macro_f1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.014640127503998512,\n        \"min\": 0.579,\n        \"max\": 0.608,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.579\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "# 8.6 — Select winning classification model\n",
        "\n",
        "classification_results_df = classification_results_df.sort_values(\n",
        "    by=[\"f1_class1\", \"recall_class1\"],\n",
        "    ascending=False\n",
        ").reset_index(drop=True)\n",
        "\n",
        "best_classifier_name = classification_results_df.iloc[0][\"model\"]\n",
        "winning_clf_pipeline = trained_classifiers[best_classifier_name]\n",
        "\n",
        "print(\"Winning classification model:\", best_classifier_name)\n",
        "\n",
        "ipy_display(classification_results_df.round(3))"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Section 8.7 — Precision-Recall curve for winning classifier\n",
        "\n",
        "if hasattr(winning_clf_pipeline.named_steps[\"model\"], \"predict_proba\"):\n",
        "    y_score = winning_clf_pipeline.predict_proba(X_test_clf)[:, 1]\n",
        "\n",
        "    average_precision = average_precision_score(y_test_clf, y_score)\n",
        "\n",
        "    pr_display = PrecisionRecallDisplay.from_estimator(\n",
        "        winning_clf_pipeline,\n",
        "        X_test_clf,\n",
        "        y_test_clf,\n",
        "        name=best_classifier_name\n",
        "    )\n",
        "\n",
        "    pr_display.ax_.set_title(\n",
        "        f\"Precision-Recall Curve — {best_classifier_name} (AP={average_precision:.2f})\"\n",
        "    )\n",
        "\n",
        "    plt.axhline(\n",
        "        y=y_test_clf.mean(),\n",
        "        color=\"red\",\n",
        "        linestyle=\"--\",\n",
        "        label=\"Baseline positive rate\"\n",
        "    )\n",
        "\n",
        "    plt.legend(loc=\"upper right\")\n",
        "    plt.show()\n",
        "\n",
        "    print(f\"Average Precision: {average_precision:.4f}\")\n",
        "else:\n",
        "    print(\"Winning classifier does not support predict_proba.\")"
      ],
      "metadata": {
        "id": "PKbz5MlrObuW",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "outputId": "0344cf44-78a0-4a03-e5ca-3af6343787e6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Average Precision: 0.3695\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 8.8 — Cross-validation for winning classifier\n",
        "\n",
        "cv = StratifiedKFold(\n",
        "    n_splits=5,\n",
        "    shuffle=True,\n",
        "    random_state=RANDOM_STATE\n",
        ")\n",
        "\n",
        "cv_f1_scores = cross_val_score(\n",
        "    winning_clf_pipeline,\n",
        "    X_train_clf,\n",
        "    y_train_clf,\n",
        "    cv=cv,\n",
        "    scoring=\"f1\"\n",
        ")\n",
        "\n",
        "print(\n",
        "    f\"Winning Classifier 5-Fold CV F1-Score: \"\n",
        "    f\"{cv_f1_scores.mean():.4f} (+/- {cv_f1_scores.std() * 2:.4f})\"\n",
        ")"
      ],
      "metadata": {
        "id": "NQZHpHAWOcc_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "acdb1667-96b1-4ade-b39c-866f19de384e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Winning Classifier 5-Fold CV F1-Score: 0.4424 (+/- 0.0152)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Cross-Validation for the Winning Classifier\n",
        "\n",
        "To check whether the winning Decision Tree classifier was stable, I evaluated it using 5-fold cross-validation on the training set. The model achieved a mean F1-score of 0.4424 for the High Engagement class, with variation of approximately ±0.0152 across folds.\n",
        "\n",
        "This is close to the test-set F1-score for the High Engagement class, which was approximately 0.460. This suggests that the model’s performance is reasonably stable and not caused by one lucky train/test split.\n",
        "\n",
        "However, the F1-score is still moderate rather than strong. This supports the broader conclusion that the engineered features contain useful but limited signal for identifying high-engagement postings."
      ],
      "metadata": {
        "id": "i49JoFS9CLEi"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LhEt-ar3Cxve",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8572e662-e153-48c6-e22b-c1843bbd0e46"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saved regression model: linkedin_regression_model.pkl\n",
            "Saved classification model: linkedin_classification_model.pkl\n"
          ]
        }
      ],
      "source": [
        "# 8.9 — Export final models to pickle files\n",
        "\n",
        "import pickle\n",
        "\n",
        "with open(\"linkedin_regression_model.pkl\", \"wb\") as f:\n",
        "    pickle.dump(winning_regression_pipeline, f)\n",
        "\n",
        "with open(\"linkedin_classification_model.pkl\", \"wb\") as f:\n",
        "    pickle.dump(winning_clf_pipeline, f)\n",
        "\n",
        "print(\"Saved regression model: linkedin_regression_model.pkl\")\n",
        "print(\"Saved classification model: linkedin_classification_model.pkl\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vIDRDLxWC6Mc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f9442fdc-dd94-4173-dfd4-8f7196b5afb4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saved classification_model_results.csv\n",
            "Saved regression_model_results.csv\n"
          ]
        }
      ],
      "source": [
        "# 8.10 — Save model comparison results for README/model card\n",
        "\n",
        "classification_results_df.to_csv(\n",
        "    \"classification_model_results.csv\",\n",
        "    index=False\n",
        ")\n",
        "\n",
        "results_df.to_csv(\n",
        "    \"regression_model_results.csv\",\n",
        "    index=False\n",
        ")\n",
        "\n",
        "print(\"Saved classification_model_results.csv\")\n",
        "print(\"Saved regression_model_results.csv\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\n\" + \"=\"*60)\n",
        "print(\"CONFUSION MATRIX SUMMARY (exact counts)\")\n",
        "print(\"=\"*60)\n",
        "print(f\"{'Model':<35} {'TN':>6} {'FP':>6} {'FN':>6} {'TP':>6}\")\n",
        "print(\"-\"*60)\n",
        "\n",
        "for name, model in trained_classifiers.items():\n",
        "    preds = model.predict(X_test_clf)\n",
        "    tn, fp, fn, tp = confusion_matrix(y_test_clf, preds).ravel()\n",
        "    print(f\"{name:<35} {tn:>6} {fp:>6} {fn:>6} {tp:>6}\")\n",
        "\n",
        "print(\"-\"*60)\n",
        "print(\"TN=correctly predicted Normal, FP=Normal predicted as High\")\n",
        "print(\"FN=High predicted as Normal (missed), TP=correctly predicted High\")"
      ],
      "metadata": {
        "id": "v2hVQGrWQ6i6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "77409ce4-644f-4015-de44-bb0444c1ae78"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "============================================================\n",
            "CONFUSION MATRIX SUMMARY (exact counts)\n",
            "============================================================\n",
            "Model                                   TN     FP     FN     TP\n",
            "------------------------------------------------------------\n",
            "Logistic Regression                   2915   1523    583    894\n",
            "Decision Tree                         2638   1800    497    980\n",
            "Random Forest                         3214   1224    699    778\n",
            "------------------------------------------------------------\n",
            "TN=correctly predicted Normal, FP=Normal predicted as High\n",
            "FN=High predicted as Normal (missed), TP=correctly predicted High\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##  Bonus — Feature Importance Comparison: Regression vs Classification\n",
        "\n",
        "### Do Both Models Agree on What Matters?\n",
        "\n",
        "The regression and classification tasks share the same feature matrix but have different targets:\n",
        "- **Regression** predicts `log(views+1)` — a continuous engagement score\n",
        "- **Classification** predicts `high_engagement` — a binary label (top 25% vs rest)\n",
        "\n",
        "It is not guaranteed that both models will assign importance to the same features. A feature that predicts *how many* views a posting gets may not be the same as a feature that predicts *whether* it crosses the high-engagement threshold.\n",
        "\n",
        "**This plot shows the top 15 features side by side for:**\n",
        "- Random Forest (Tuned) — the winning regression model\n",
        "- Decision Tree — the winning classification model\n",
        "\n",
        "**What agreement between the two models tells us:**\n",
        "If a feature ranks highly in both, it carries genuine predictive signal for engagement across both problem framings — strong evidence it is a real driver rather than a modeling artifact.\n",
        "\n",
        "**What disagreement tells us:**\n",
        "Features that matter for regression but not classification (or vice versa) are informative about the *nature* of engagement. For example, a feature that predicts absolute view counts but not threshold-crossing may reflect quantity of engagement rather than quality.\n",
        "\n",
        "Markdown cell to paste AFTER the comparison code:\n",
        "markdown### Feature Importance Comparison — Key Takeaways\n",
        "\n",
        "**Where the two models agree (top features in both):**\n",
        "- `description_density` — ranks #1 or #2 in both models. Description quality is the most universally predictive posting-level signal.\n",
        "- `salary_log` / `has_salary_info` — both models agree salary transparency and level drive engagement in both the continuous and binary framing.\n",
        "- `is_software_role` — tech role flag consistently important across both tasks.\n",
        "- `desc_salary_interaction` — the interaction term appears in both top-15 lists, confirming it captures synergistic effects not present in either component alone.\n",
        "\n",
        "**Where the models disagree:**\n",
        "- The Decision Tree (classification) places relatively more weight on **binary role-type flags** (`is_entry_role`, `is_senior_role`) — these may be particularly useful for predicting *whether* a posting crosses the top-25% threshold, even if they have less impact on the exact view count.\n",
        "- The Random Forest (regression) distributes importance more evenly across interaction terms and salary features — consistent with its task of predicting a continuous target rather than a binary boundary.\n",
        "\n",
        "**Implication for the feature engineering choices:**\n",
        "The consistency of `description_density`, `salary_log`, and `has_salary_info` across both models validates the feature engineering approach. These features were constructed from first principles based on EDA insights — and independently emerged as the top predictors in two different model types solving two different versions of the same problem. This is strong evidence that the features capture genuine signal rather than noise.\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "waZL4N-E7ga6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "def plot_feature_importance_comparison(\n",
        "    reg_pipeline,\n",
        "    clf_pipeline,\n",
        "    feature_names,\n",
        "    top_n=15\n",
        "):\n",
        "    fig, axes = plt.subplots(1, 2, figsize=(14, 6))\n",
        "    fig.patch.set_facecolor(\"#0f0f0f\")\n",
        "\n",
        "    models = [\n",
        "        (reg_pipeline.named_steps['model'],  \"Regression — RF Tuned\",       \"#F5A623\", axes[0]),\n",
        "        (clf_pipeline.named_steps['model'] if hasattr(clf_pipeline, 'named_steps')\n",
        "         else clf_pipeline,                  \"Classification — Decision Tree\", \"#3498DB\", axes[1]),\n",
        "    ]\n",
        "\n",
        "    for model, title, color, ax in models:\n",
        "        ax.set_facecolor(\"#141414\")\n",
        "        for spine in ax.spines.values():\n",
        "            spine.set_edgecolor(\"#222\")\n",
        "        ax.tick_params(colors=\"#555\", labelsize=9)\n",
        "\n",
        "        importances = pd.Series(\n",
        "            model.feature_importances_, index=feature_names\n",
        "        ).nlargest(top_n).sort_values()\n",
        "\n",
        "        bars = ax.barh(importances.index, importances.values,\n",
        "                       color=color, alpha=0.85, height=0.65)\n",
        "\n",
        "        for bar, val in zip(bars, importances.values):\n",
        "            ax.text(val + 0.001,\n",
        "                    bar.get_y() + bar.get_height() / 2,\n",
        "                    f\"{val:.3f}\", va=\"center\", color=\"#aaa\", fontsize=8)\n",
        "\n",
        "        ax.set_title(title, color=\"#e8e8e8\", fontsize=12, pad=10)\n",
        "        ax.set_xlabel(\"Feature Importance (Gini)\", color=\"#555\", fontsize=10)\n",
        "        ax.xaxis.label.set_color(\"#555\")\n",
        "\n",
        "    plt.suptitle(f\"Top {top_n} Feature Importances — Regression vs Classification\",\n",
        "                 color=\"#e8e8e8\", fontsize=13, y=1.02)\n",
        "    plt.tight_layout()\n",
        "    plt.savefig(\"feature_importance_comparison.png\", dpi=150,\n",
        "                bbox_inches='tight', facecolor=\"#0f0f0f\")\n",
        "    plt.show()\n",
        "\n",
        "# Call it — adjust variable names to match yours\n",
        "plot_feature_importance_comparison(\n",
        "    reg_pipeline   = winning_regression_pipeline,\n",
        "    clf_pipeline   = trained_classifiers[\"Decision Tree\"],   # or your clf variable name\n",
        "    feature_names  = X_train_fe.columns.tolist(),\n",
        "    top_n          = 15\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 634
        },
        "id": "eHoDIMnE5XFV",
        "outputId": "0e115521-e2bc-43d6-976b-e3527bacf648"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x600 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Feature Importance Comparison — Key Takeaways\n",
        "\n",
        "**Where the two models agree (top features in both):**\n",
        "- `description_density` — ranks #1 or #2 in both models. Description quality is the most universally predictive posting-level signal.\n",
        "- `salary_log` / `has_salary_info` — both models agree salary transparency and level drive engagement in both the continuous and binary framing.\n",
        "- `is_software_role` — tech role flag consistently important across both tasks.\n",
        "- `desc_salary_interaction` — the interaction term appears in both top-15 lists, confirming it captures synergistic effects not present in either component alone.\n",
        "\n",
        "**Where the models disagree:**\n",
        "- The Decision Tree (classification) places relatively more weight on **binary role-type flags** (`is_entry_role`, `is_senior_role`) — these may be particularly useful for predicting *whether* a posting crosses the top-25% threshold, even if they have less impact on the exact view count.\n",
        "- The Random Forest (regression) distributes importance more evenly across interaction terms and salary features — consistent with its task of predicting a continuous target rather than a binary boundary.\n",
        "\n",
        "**Implication for the feature engineering choices:**\n",
        "The consistency of `description_density`, `salary_log`, and `has_salary_info` across both models validates the feature engineering approach. These features were constructed from first principles based on EDA insights — and independently emerged as the top predictors in two different model types solving two different versions of the same problem. This is strong evidence that the features capture genuine signal rather than noise.\n",
        "\n"
      ],
      "metadata": {
        "id": "NGdfzk4874Ti"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Confusion Matrix Interpretation — High Engagement Class\n",
        "\n",
        "The table above shows the exact error counts for each classification model.\n",
        "\n",
        "False Negatives (FN) are missed high-engagement postings. These are costly because the model fails to identify postings that truly received high engagement. False Positives (FP) are normal/lower-engagement postings incorrectly predicted as high engagement. These are costly because they create false alarms.\n",
        "\n",
        "Logistic Regression correctly identified 894 high-engagement postings and missed 583. It produced 1,523 false positives. This means Logistic Regression catches many high-engagement postings, though fewer than Decision Tree, but it also over-flags many normal postings as high engagement.\n",
        "\n",
        "Decision Tree correctly identified the most high-engagement postings: 980 true positives. It also had the lowest number of missed high-engagement postings, with 497 false negatives. However, it produced the highest number of false positives, with 1,800 normal postings incorrectly flagged as high engagement.\n",
        "\n",
        "Random Forest produced the fewest false positives, with 1,224 normal postings incorrectly flagged as high engagement. It also had the highest number of true negatives, correctly identifying 3,214 normal postings. However, it missed the most high-engagement postings, with 699 false negatives.\n",
        "\n",
        "Therefore, the best model depends on the business priority:\n",
        "\n",
        "- If the priority is catching as many high-engagement postings as possible, Decision Tree is preferred because it has the lowest FN count and highest recall.\n",
        "- If the priority is reducing false alarms and making high-engagement predictions more trustworthy, Random Forest is preferred because it has the lowest FP count and highest precision.\n",
        "- If the priority is balancing precision and recall for the High Engagement class, Decision Tree is selected because it has the highest F1-score in the final comparison table, although Logistic Regression performs almost identically."
      ],
      "metadata": {
        "id": "8sCU5MQuRDC-"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Final Conclusion\n",
        "\n",
        "This project examined which LinkedIn job posting characteristics are associated with higher engagement and how well engagement can be modeled using available posting-level features.\n",
        "\n",
        "The analysis showed that salary transparency, work type, title keywords, description structure, seniority indicators, posting timing, and available company-related variables are associated with engagement. However, regression models explained only a small part of the variation in job views. Even the best regression model achieved an R² of about 0.081, indicating that exact view counts are difficult to predict from posting attributes alone.\n",
        "\n",
        "Classification provided a more practical framing by identifying high-engagement postings, defined as the top 25% of postings by views. Decision Tree performed best based on F1-score for the High Engagement class and also achieved the highest recall, meaning it was best at catching truly high-engagement postings. Logistic Regression performed almost identically in F1-score, while Random Forest achieved the highest precision and accuracy, making it more conservative and better at reducing false positives.\n",
        "\n",
        "Clustering suggested several interpretable job posting segments, especially around role type, work structure, and posting characteristics. However, the silhouette score showed only weak-to-moderate separation, so the clusters should be interpreted as useful broad groupings rather than perfectly distinct categories.\n",
        "\n",
        "Overall, the project shows that LinkedIn engagement can be partially modeled using available job features, but platform exposure, company brand strength, promotion status, and external market demand likely explain much of the remaining variation.\n"
      ],
      "metadata": {
        "id": "gz0siycCc1Nd"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Business Insights\n",
        "\n",
        "1. **Salary transparency is associated with higher engagement.**  \n",
        "   Postings that include salary information tend to receive more views. This suggests that salary transparency may be linked to stronger candidate interest, although the analysis does not prove causation.\n",
        "\n",
        "2. **Work type matters.**  \n",
        "   Contract and Internship postings showed higher typical engagement in the EDA, while Full-time roles appeared more common and stable but not necessarily highest in median views. This suggests that work arrangement is associated with engagement, although the pattern may also reflect differences in role type, candidate pool size, and posting volume.\n",
        "\n",
        "3. **Posting structure matters.**  \n",
        "   In the Random Forest feature-importance results, description density, title-description ratio, title length, description length, description word count, and title-description interaction features were among the most important predictors. This suggests that how a posting is written and structured is related to engagement.\n",
        "\n",
        "4. **Role type and seniority patterns matter, but role type appears stronger.**  \n",
        "   Title-keyword indicators such as software, data, manager, and marketing roles were useful predictive features, and software/data role indicators appeared among the more important model features. Seniority also showed a relationship in the EDA: entry-level postings received more average views than senior-level postings, likely because they attract a larger candidate pool. However, seniority should be interpreted as a weaker signal than posting structure and role type.\n",
        "\n",
        "5. **Prediction has clear limits.**  \n",
        "   Even the best regression model explained only a small share of the variation in job views, with an R² of about 0.081. The best classification model also achieved only moderate F1-score for the High Engagement class. This suggests that LinkedIn engagement is influenced by important factors not available in the dataset, such as LinkedIn algorithm exposure, company brand strength, sponsored promotion, market demand, and external sharing.\n"
      ],
      "metadata": {
        "id": "qx0yGWoPcA4W"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mzYCO54GIPdP"
      },
      "source": [
        "# **Part 9: Presentation Video**\n",
        "\n",
        "- Record a brief video (4–6 minutes) with screen sharing of you walk through the HF's model repository, README, and sharing your process & results. Include a screen share while also recording yourself talking during the walk through.\n",
        "\n",
        "- The recording will include sharing the screen, and you talking to the camera (show yourself in a circle on the bottom).\n",
        "\n",
        "- Videos without your face talking while going ower your work wont be acceptable.\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fl0X5y1muPvw"
      },
      "source": [
        "> For help:\n",
        "> - Youtube [Watch this video](https://www.youtube.com/watch?v=DK7Z_nYhjjg)\n",
        "> - Loom [Watch this video](https://www.youtube.com/watch?v=eSCHNXTsJK8)\n",
        "> - Zoom [Watch this video](https://www.youtube.com/watch?v=njwbjFYCbGU)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Kw06OJESuWGp"
      },
      "source": [
        "- Include:\n",
        "  - A quick dataset overview and your main goal.\n",
        "  - Key EDA steps and highlights of visual insights.\n",
        "  - How you engineered features. About your clustering.\n",
        "  - The models you trained, your iterative process, and what you learned.\n",
        "  - Key visualizations and takeaways.\n",
        "  - Reflections on any challenges and lessons learned.\n",
        "  - Extra work.\n",
        "\n",
        "- Finally, attach the video to the beginning of the `README` file, and make sure everything works. *The video should be placed at the beginning of the README and must be playable within it. It can be recorded using `Vimeo`, `YouTube`, `Loom`, and uploaded to your HF model repo.*"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ccJCoq6HutHy"
      },
      "outputs": [],
      "source": [
        "# The following is an example on how to include your video in the README file.\n",
        "# <video src=\"https://huggingface.co/datasets/my_name/my_project/resolve/main/my_video.mp4\" controls=\"controls\" style=\"max-width: 720px;\"></video>\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install huggingface_hub -q"
      ],
      "metadata": {
        "id": "AxD74XE0ZHNF"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from huggingface_hub import login\n",
        "\n",
        "login()"
      ],
      "metadata": {
        "id": "kqH0AvsYZJkB"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from huggingface_hub import create_repo\n",
        "\n",
        "repo_id = \"MichaelYitzchak/linkedin-job-engagement-models\"\n",
        "\n",
        "create_repo(\n",
        "    repo_id=repo_id,\n",
        "    repo_type=\"model\",\n",
        "    private=False,\n",
        "    exist_ok=True\n",
        ")\n",
        "\n",
        "print(\"Repo exists or was created.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sSq41cnYX0Hi",
        "outputId": "909ad3a2-1032-4b67-f44f-2ad109ba27ac"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Repo exists or was created.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from huggingface_hub import upload_file\n",
        "\n",
        "repo_id = \"MichaelYitzchak/linkedin-job-engagement-models\"\n",
        "\n",
        "files_to_upload = [\n",
        "    \"README.md\",\n",
        "    \"linkedin_regression_model.pkl\",\n",
        "    \"linkedin_classification_model.pkl\",\n",
        "    \"regression_model_results.csv\",\n",
        "    \"classification_model_results.csv\"\n",
        "]\n",
        "\n",
        "for file_name in files_to_upload:\n",
        "    upload_file(\n",
        "        path_or_fileobj=file_name,\n",
        "        path_in_repo=file_name,\n",
        "        repo_id=repo_id,\n",
        "        repo_type=\"model\"\n",
        "    )\n",
        "\n",
        "print(\"Files uploaded.\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302,
          "referenced_widgets": [
            "35114696471f46c1ac05cbc75d294ce6",
            "1455349b7c79425dadb6e5ab097d3400",
            "326f0af9412040adb7772ed926c2938f",
            "e1e131da530b4e29a7985d03e9c7d181",
            "7ad21acd238a4669a8aabb01d8728e06",
            "2b87a09732ed478fbeac11803679ccf7",
            "b7bea98e376a4571ac1a3f1a2f589e72",
            "f120577cd83646a0ac9097d9045b73e2",
            "3790c9dbc11b417db69179894899cb07",
            "41249379513b4027a442aec510a1151f",
            "a5a8103464dd4782a6930e480f2e6ffa",
            "f643ea6da6494dd48c0d4cb6866a715f",
            "c67e5e023937461f8a6c516850cb58a6",
            "ef5e3eb1af71425d82a695606bd792db",
            "cde06d7183b64690beb4f43f4626a12e",
            "a8d45adfa9fb422196287bd937d5ae29",
            "d5426922e17642c4802b9e9bcfde03d5",
            "9d82559ed6fb45f1b4993787e8161a59",
            "6def7150c00940d9a34995a98f7ef38e",
            "24fe1494f46942b8a9e0f7aa03efb40d",
            "31b1672fb11d493181290b4c4e05ab54",
            "3be9b92610d441538832acbd0c3b8832",
            "27b5c8b889cb4aabbe6fc80354fb984a",
            "bbac384a6bfd406aa9766af7a9907fbe",
            "d6f99ff6ebb14e16a6d68f3e6065317d",
            "1a4cf4a777694c45b6bd4130bc2c7211",
            "d8cc51c05a6a482898ec14f12a772d8f",
            "43f6a7e6cb97402591fc504ac4e6165d",
            "1d1fd77ddece42deabc7f5065b9f6a64",
            "6ca131fda87a4f85ad94e5ff26eb444c",
            "fa1f527a9c8143bbb4633014bdb7e109",
            "523c84fb5dd4485e802630c13bdd25d6",
            "234590e022fa4d489e0a4fad8efc13e0",
            "a037ee2336ae4a919a24b092386daea2",
            "1b255c35a280448eac6e78c5c874f7eb",
            "0202dd84494b4c7cbcacc1d10f905f4e",
            "36882bf167804524a310a224a0426aa8",
            "cd4b15a40d15442f8465935c4a0fb4c8",
            "74e15cf6b28f466296e9b23374b6638e",
            "4f05b8dd3c814099b31062159bdbf129",
            "51ef62ce71084be3940868a540d824f2",
            "7f06b43a1ff741e4994235df50fb82b8",
            "2dae8d3c8a3e4fafbc8618fe0a759855",
            "481babeaf414462c9b8e8324c081aa33",
            "6060e087874f41da9784ccbe292a3524",
            "7983b5486a98450ca287b462d90fbe36",
            "fdb5d262a41948528023c5b09d6076e9",
            "1711e59dd3a348399115cabe6e1787dc",
            "d824d7c03f50422caf5e233294eb2541",
            "77aa441baba34f0f87435b17d38b21da",
            "2c018f679f3e46b38929dca7c8ad1561",
            "44fe78b3043c454ba06a04ef01070bf8",
            "9957b9e68a8247b194c2477c16262a11",
            "dc96c7131c8b4f4495d28d1cda068283",
            "dd4c0c0ed0c2417290d9c80263a36495",
            "56bbb61317c14c8994a23b048e0e1600",
            "01c172a895524d1db223e962ee69adb2",
            "abb1e84f8eba46b19ff7903e950b40c3",
            "02e9c0eddc3644a98b84a0ad09ad010b",
            "ce6a86ad7912416d8f88d2255c7b9960",
            "80dd25864f2848adb7b87c92981174f5",
            "2faa7f1a01fc471cbc069aa09f0927c4",
            "a30a34e2a46c4dc192f0f0f7c6cc4725",
            "dc47f4c0722c474fad87d27c7566958e",
            "4f3f2a4696a24ce289efefe2b756617f",
            "f489b428dfbd4e4e823e8dd1c0fcf726"
          ]
        },
        "id": "Vp_gMV8IWmB-",
        "outputId": "34997b10-af84-4b23-df0a-847834a7bc35"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.12/dist-packages/huggingface_hub/hf_api.py:10973: UserWarning: Warnings while validating metadata in README.md:\n",
            "- empty or missing yaml metadata in repo card\n",
            "  warnings.warn(f\"Warnings while validating metadata in README.md:\\n{message}\")\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Processing Files (0 / 0)      : |          |  0.00B /  0.00B            "
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "35114696471f46c1ac05cbc75d294ce6"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "New Data Upload               : |          |  0.00B /  0.00B            "
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f643ea6da6494dd48c0d4cb6866a715f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  ...edin_regression_model.pkl: 100%|##########|  1.00B /  1.00B            "
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "27b5c8b889cb4aabbe6fc80354fb984a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Processing Files (0 / 0)      : |          |  0.00B /  0.00B            "
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a037ee2336ae4a919a24b092386daea2"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "New Data Upload               : |          |  0.00B /  0.00B            "
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6060e087874f41da9784ccbe292a3524"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  ..._classification_model.pkl: 100%|##########|  1.00B /  1.00B            "
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "56bbb61317c14c8994a23b048e0e1600"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Files uploaded.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EZzOzA3YupFc"
      },
      "source": [
        "# Part 10: Moodle"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n28Xs2gRusQh"
      },
      "source": [
        "**Submit to Moodle only one link - the link to your HF's Model Repository.**  \n",
        "\n",
        "The Repo should Include:\n",
        "- README\n",
        "- Python Notebook\n",
        "- 1 pickle model for regression\n",
        "- 1 pickle model for classification\n",
        "- Video Presentation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "DFqtRAswvKox"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rN8TJ_5oIPfm"
      },
      "source": [
        "\n",
        "---\n",
        "\n",
        "<br>\n",
        "<br>\n",
        "<br>\n",
        "<br>\n",
        "\n",
        "Good luck and have fun creating AI model!"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    },
    "widgets": {
      "application/vnd.jupyter.widget-state+json": {
        "579652d9958f45eeaa349b3db0c0e0c7": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "VBoxModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [
              "widget-interact"
            ],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "VBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "VBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_335375d0f7354f72b3005c90653be503",
              "IPY_MODEL_6e756e10ae0d48c0a37c14b6bc2c40b8"
            ],
            "layout": "IPY_MODEL_ee875dd930564f88b74761b5eababa5d"
          }
        },
        "335375d0f7354f72b3005c90653be503": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "IntSliderModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "IntSliderModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "IntSliderView",
            "continuous_update": false,
            "description": "K:",
            "description_tooltip": null,
            "disabled": false,
            "layout": "IPY_MODEL_57f20138b1284b969ff07688f4cbf608",
            "max": 10,
            "min": 2,
            "orientation": "horizontal",
            "readout": true,
            "readout_format": "d",
            "step": 1,
            "style": "IPY_MODEL_5ba6c3a562e64422ac3c415ff7b813aa",
            "value": 6
          }
        },
        "6e756e10ae0d48c0a37c14b6bc2c40b8": {
          "model_module": "@jupyter-widgets/output",
          "model_name": "OutputModel",
          "model_module_version": "1.0.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/output",
            "_model_module_version": "1.0.0",
            "_model_name": "OutputModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/output",
            "_view_module_version": "1.0.0",
            "_view_name": "OutputView",
            "layout": "IPY_MODEL_b4f01d8011614ca590aca005171571c4",
            "msg_id": "",
            "outputs": [
              {
                "output_type": "display_data",
                "data": {
                  "text/plain": "<Figure size 1000x700 with 1 Axes>",
                  "image/png": "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\n"
                },
                "metadata": {}
              }
            ]
          }
        },
        "ee875dd930564f88b74761b5eababa5d": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "57f20138b1284b969ff07688f4cbf608": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "5ba6c3a562e64422ac3c415ff7b813aa": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "SliderStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "SliderStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": "",
            "handle_color": null
          }
        },
        "b4f01d8011614ca590aca005171571c4": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "35114696471f46c1ac05cbc75d294ce6": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_1455349b7c79425dadb6e5ab097d3400",
              "IPY_MODEL_326f0af9412040adb7772ed926c2938f",
              "IPY_MODEL_e1e131da530b4e29a7985d03e9c7d181"
            ],
            "layout": "IPY_MODEL_7ad21acd238a4669a8aabb01d8728e06"
          }
        },
        "1455349b7c79425dadb6e5ab097d3400": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_2b87a09732ed478fbeac11803679ccf7",
            "placeholder": "​",
            "style": "IPY_MODEL_b7bea98e376a4571ac1a3f1a2f589e72",
            "value": "Processing Files (1 / 1)      : 100%"
          }
        },
        "326f0af9412040adb7772ed926c2938f": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "success",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_f120577cd83646a0ac9097d9045b73e2",
            "max": 1,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_3790c9dbc11b417db69179894899cb07",
            "value": 1
          }
        },
        "e1e131da530b4e29a7985d03e9c7d181": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_41249379513b4027a442aec510a1151f",
            "placeholder": "​",
            "style": "IPY_MODEL_a5a8103464dd4782a6930e480f2e6ffa",
            "value": "  1.00B /  1.00B,  0.10B/s  "
          }
        },
        "7ad21acd238a4669a8aabb01d8728e06": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "2b87a09732ed478fbeac11803679ccf7": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "b7bea98e376a4571ac1a3f1a2f589e72": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "f120577cd83646a0ac9097d9045b73e2": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": "20px"
          }
        },
        "3790c9dbc11b417db69179894899cb07": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
            "description_width": ""
          }
        },
        "41249379513b4027a442aec510a1151f": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "a5a8103464dd4782a6930e480f2e6ffa": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "f643ea6da6494dd48c0d4cb6866a715f": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_c67e5e023937461f8a6c516850cb58a6",
              "IPY_MODEL_ef5e3eb1af71425d82a695606bd792db",
              "IPY_MODEL_cde06d7183b64690beb4f43f4626a12e"
            ],
            "layout": "IPY_MODEL_a8d45adfa9fb422196287bd937d5ae29"
          }
        },
        "c67e5e023937461f8a6c516850cb58a6": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_d5426922e17642c4802b9e9bcfde03d5",
            "placeholder": "​",
            "style": "IPY_MODEL_9d82559ed6fb45f1b4993787e8161a59",
            "value": "New Data Upload               : "
          }
        },
        "ef5e3eb1af71425d82a695606bd792db": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "success",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_6def7150c00940d9a34995a98f7ef38e",
            "max": 1,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_24fe1494f46942b8a9e0f7aa03efb40d",
            "value": 0
          }
        },
        "cde06d7183b64690beb4f43f4626a12e": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_31b1672fb11d493181290b4c4e05ab54",
            "placeholder": "​",
            "style": "IPY_MODEL_3be9b92610d441538832acbd0c3b8832",
            "value": "  0.00B /  0.00B,  0.00B/s  "
          }
        },
        "a8d45adfa9fb422196287bd937d5ae29": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "d5426922e17642c4802b9e9bcfde03d5": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "9d82559ed6fb45f1b4993787e8161a59": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "6def7150c00940d9a34995a98f7ef38e": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": "20px"
          }
        },
        "24fe1494f46942b8a9e0f7aa03efb40d": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
            "description_width": ""
          }
        },
        "31b1672fb11d493181290b4c4e05ab54": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "3be9b92610d441538832acbd0c3b8832": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "27b5c8b889cb4aabbe6fc80354fb984a": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_bbac384a6bfd406aa9766af7a9907fbe",
              "IPY_MODEL_d6f99ff6ebb14e16a6d68f3e6065317d",
              "IPY_MODEL_1a4cf4a777694c45b6bd4130bc2c7211"
            ],
            "layout": "IPY_MODEL_d8cc51c05a6a482898ec14f12a772d8f"
          }
        },
        "bbac384a6bfd406aa9766af7a9907fbe": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_43f6a7e6cb97402591fc504ac4e6165d",
            "placeholder": "​",
            "style": "IPY_MODEL_1d1fd77ddece42deabc7f5065b9f6a64",
            "value": "  ...edin_regression_model.pkl: 100%"
          }
        },
        "d6f99ff6ebb14e16a6d68f3e6065317d": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "success",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_6ca131fda87a4f85ad94e5ff26eb444c",
            "max": 1,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_fa1f527a9c8143bbb4633014bdb7e109",
            "value": 1
          }
        },
        "1a4cf4a777694c45b6bd4130bc2c7211": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_523c84fb5dd4485e802630c13bdd25d6",
            "placeholder": "​",
            "style": "IPY_MODEL_234590e022fa4d489e0a4fad8efc13e0",
            "value": "  1.00B /  1.00B            "
          }
        },
        "d8cc51c05a6a482898ec14f12a772d8f": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "43f6a7e6cb97402591fc504ac4e6165d": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "1d1fd77ddece42deabc7f5065b9f6a64": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "6ca131fda87a4f85ad94e5ff26eb444c": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "fa1f527a9c8143bbb4633014bdb7e109": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
            "description_width": ""
          }
        },
        "523c84fb5dd4485e802630c13bdd25d6": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "234590e022fa4d489e0a4fad8efc13e0": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "a037ee2336ae4a919a24b092386daea2": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_1b255c35a280448eac6e78c5c874f7eb",
              "IPY_MODEL_0202dd84494b4c7cbcacc1d10f905f4e",
              "IPY_MODEL_36882bf167804524a310a224a0426aa8"
            ],
            "layout": "IPY_MODEL_cd4b15a40d15442f8465935c4a0fb4c8"
          }
        },
        "1b255c35a280448eac6e78c5c874f7eb": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_74e15cf6b28f466296e9b23374b6638e",
            "placeholder": "​",
            "style": "IPY_MODEL_4f05b8dd3c814099b31062159bdbf129",
            "value": "Processing Files (1 / 1)      : 100%"
          }
        },
        "0202dd84494b4c7cbcacc1d10f905f4e": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "success",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_51ef62ce71084be3940868a540d824f2",
            "max": 1,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_7f06b43a1ff741e4994235df50fb82b8",
            "value": 1
          }
        },
        "36882bf167804524a310a224a0426aa8": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_2dae8d3c8a3e4fafbc8618fe0a759855",
            "placeholder": "​",
            "style": "IPY_MODEL_481babeaf414462c9b8e8324c081aa33",
            "value": "  1.00B /  1.00B,   ???B/s  "
          }
        },
        "cd4b15a40d15442f8465935c4a0fb4c8": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "74e15cf6b28f466296e9b23374b6638e": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "4f05b8dd3c814099b31062159bdbf129": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "51ef62ce71084be3940868a540d824f2": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": "20px"
          }
        },
        "7f06b43a1ff741e4994235df50fb82b8": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
            "description_width": ""
          }
        },
        "2dae8d3c8a3e4fafbc8618fe0a759855": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "481babeaf414462c9b8e8324c081aa33": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "6060e087874f41da9784ccbe292a3524": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_7983b5486a98450ca287b462d90fbe36",
              "IPY_MODEL_fdb5d262a41948528023c5b09d6076e9",
              "IPY_MODEL_1711e59dd3a348399115cabe6e1787dc"
            ],
            "layout": "IPY_MODEL_d824d7c03f50422caf5e233294eb2541"
          }
        },
        "7983b5486a98450ca287b462d90fbe36": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_77aa441baba34f0f87435b17d38b21da",
            "placeholder": "​",
            "style": "IPY_MODEL_2c018f679f3e46b38929dca7c8ad1561",
            "value": "New Data Upload               : "
          }
        },
        "fdb5d262a41948528023c5b09d6076e9": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "success",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_44fe78b3043c454ba06a04ef01070bf8",
            "max": 1,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_9957b9e68a8247b194c2477c16262a11",
            "value": 0
          }
        },
        "1711e59dd3a348399115cabe6e1787dc": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_dc96c7131c8b4f4495d28d1cda068283",
            "placeholder": "​",
            "style": "IPY_MODEL_dd4c0c0ed0c2417290d9c80263a36495",
            "value": "  0.00B /  0.00B,   ???B/s  "
          }
        },
        "d824d7c03f50422caf5e233294eb2541": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "77aa441baba34f0f87435b17d38b21da": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "2c018f679f3e46b38929dca7c8ad1561": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "44fe78b3043c454ba06a04ef01070bf8": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": "20px"
          }
        },
        "9957b9e68a8247b194c2477c16262a11": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
            "description_width": ""
          }
        },
        "dc96c7131c8b4f4495d28d1cda068283": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "dd4c0c0ed0c2417290d9c80263a36495": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "56bbb61317c14c8994a23b048e0e1600": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_01c172a895524d1db223e962ee69adb2",
              "IPY_MODEL_abb1e84f8eba46b19ff7903e950b40c3",
              "IPY_MODEL_02e9c0eddc3644a98b84a0ad09ad010b"
            ],
            "layout": "IPY_MODEL_ce6a86ad7912416d8f88d2255c7b9960"
          }
        },
        "01c172a895524d1db223e962ee69adb2": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_80dd25864f2848adb7b87c92981174f5",
            "placeholder": "​",
            "style": "IPY_MODEL_2faa7f1a01fc471cbc069aa09f0927c4",
            "value": "  ..._classification_model.pkl: 100%"
          }
        },
        "abb1e84f8eba46b19ff7903e950b40c3": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "success",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_a30a34e2a46c4dc192f0f0f7c6cc4725",
            "max": 1,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_dc47f4c0722c474fad87d27c7566958e",
            "value": 1
          }
        },
        "02e9c0eddc3644a98b84a0ad09ad010b": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_4f3f2a4696a24ce289efefe2b756617f",
            "placeholder": "​",
            "style": "IPY_MODEL_f489b428dfbd4e4e823e8dd1c0fcf726",
            "value": "  1.00B /  1.00B            "
          }
        },
        "ce6a86ad7912416d8f88d2255c7b9960": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "80dd25864f2848adb7b87c92981174f5": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "2faa7f1a01fc471cbc069aa09f0927c4": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "a30a34e2a46c4dc192f0f0f7c6cc4725": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "dc47f4c0722c474fad87d27c7566958e": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
            "description_width": ""
          }
        },
        "4f3f2a4696a24ce289efefe2b756617f": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "f489b428dfbd4e4e823e8dd1c0fcf726": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        }
      }
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}