{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "rBFKIR0rv3A4"
   },
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np \n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import openpyxl\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "BB3AXkhGHmDF"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "tdAlsxI7v7DS",
    "outputId": "9f8e9de9-d778-4fdf-b19a-c98ef96d5bb0"
   },
   "outputs": [],
   "source": [
    "\n",
    "pcos_df = pd.read_csv(\"cleandata.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "pHtH5-zsv_xQ",
    "outputId": "065dab41-05a6-43a8-e1d2-d2ca3ee3840e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 541 entries, 0 to 540\n",
      "Data columns (total 43 columns):\n",
      " #   Column                  Non-Null Count  Dtype  \n",
      "---  ------                  --------------  -----  \n",
      " 0   Unnamed: 0              541 non-null    int64  \n",
      " 1   PCOS (Y/N)              541 non-null    int64  \n",
      " 2    Age (yrs)              541 non-null    int64  \n",
      " 3   Weight (Kg)             541 non-null    float64\n",
      " 4   Height(Cm)              541 non-null    float64\n",
      " 5   BMI                     541 non-null    float64\n",
      " 6   Blood Group             541 non-null    int64  \n",
      " 7   Pulse rate(bpm)         541 non-null    int64  \n",
      " 8   RR (breaths/min)        541 non-null    int64  \n",
      " 9   Hb(g/dl)                541 non-null    float64\n",
      " 10  Cycle(R/I)              541 non-null    int64  \n",
      " 11  Cycle length(days)      541 non-null    int64  \n",
      " 12  Marraige Status (Yrs)   541 non-null    float64\n",
      " 13  Pregnant(Y/N)           541 non-null    int64  \n",
      " 14  No. of abortions        541 non-null    int64  \n",
      " 15    I   beta-HCG(mIU/mL)  541 non-null    float64\n",
      " 16  II    beta-HCG(mIU/mL)  541 non-null    float64\n",
      " 17  FSH(mIU/mL)             541 non-null    float64\n",
      " 18  LH(mIU/mL)              541 non-null    float64\n",
      " 19  FSH/LH                  541 non-null    float64\n",
      " 20  Hip(inch)               541 non-null    int64  \n",
      " 21  Waist(inch)             541 non-null    int64  \n",
      " 22  Waist:Hip Ratio         541 non-null    float64\n",
      " 23  TSH (mIU/L)             541 non-null    float64\n",
      " 24  AMH(ng/mL)              541 non-null    float64\n",
      " 25  PRL(ng/mL)              541 non-null    float64\n",
      " 26  Vit D3 (ng/mL)          541 non-null    float64\n",
      " 27  PRG(ng/mL)              541 non-null    float64\n",
      " 28  RBS(mg/dl)              541 non-null    float64\n",
      " 29  Weight gain(Y/N)        541 non-null    int64  \n",
      " 30  hair growth(Y/N)        541 non-null    int64  \n",
      " 31  Skin darkening (Y/N)    541 non-null    int64  \n",
      " 32  Hair loss(Y/N)          541 non-null    int64  \n",
      " 33  Pimples(Y/N)            541 non-null    int64  \n",
      " 34  Fast food (Y/N)         541 non-null    float64\n",
      " 35  Reg.Exercise(Y/N)       541 non-null    int64  \n",
      " 36  BP _Systolic (mmHg)     541 non-null    int64  \n",
      " 37  BP _Diastolic (mmHg)    541 non-null    int64  \n",
      " 38  Follicle No. (L)        541 non-null    int64  \n",
      " 39  Follicle No. (R)        541 non-null    int64  \n",
      " 40  Avg. F size (L) (mm)    541 non-null    float64\n",
      " 41  Avg. F size (R) (mm)    541 non-null    float64\n",
      " 42  Endometrium (mm)        541 non-null    float64\n",
      "dtypes: float64(21), int64(22)\n",
      "memory usage: 181.9 KB\n"
     ]
    }
   ],
   "source": [
    "pcos_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 507
    },
    "id": "4a0Nr3zy3iqt",
    "outputId": "56405704-8cb8-49c2-ae26-95ea6d8743f6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    541.000000\n",
      "mean      73.247689\n",
      "std        4.430285\n",
      "min       13.000000\n",
      "25%       72.000000\n",
      "50%       72.000000\n",
      "75%       74.000000\n",
      "max       82.000000\n",
      "Name: Pulse rate(bpm) , dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='Pulse rate(bpm) '>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(pcos_df['Pulse rate(bpm) '].describe())\n",
    "\n",
    "sns.boxplot(pcos_df['Pulse rate(bpm) '])\n",
    "#data is skewed so we use median "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "MYLMSMls3odB"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\User\\AppData\\Local\\Temp\\ipykernel_19708\\2434234942.py:2: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
      "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
      "\n",
      "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
      "\n",
      "\n",
      "  pcos_df['Pulse rate(bpm) '].mask(pcos_df['Pulse rate(bpm) '] < 60 ,pcos_df['Pulse rate(bpm) '].median() , inplace=True)\n"
     ]
    }
   ],
   "source": [
    "#replacing all values less than 60 with the median \n",
    "pcos_df['Pulse rate(bpm) '].mask(pcos_df['Pulse rate(bpm) '] < 60 ,pcos_df['Pulse rate(bpm) '].median() , inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 507
    },
    "id": "SnSptXFv3zy1",
    "outputId": "37760958-4321-40b9-b49a-892fc9b1e835"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    541.000000\n",
      "mean      73.456562\n",
      "std        2.686120\n",
      "min       70.000000\n",
      "25%       72.000000\n",
      "50%       72.000000\n",
      "75%       74.000000\n",
      "max       82.000000\n",
      "Name: Pulse rate(bpm) , dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='Pulse rate(bpm) '>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(pcos_df['Pulse rate(bpm) '].describe())\n",
    "\n",
    "sns.boxplot(pcos_df['Pulse rate(bpm) '])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 507
    },
    "id": "-pZheMzz30bv",
    "outputId": "e97010f8-8f16-452c-9080-1f25d11c1482"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    541.000000\n",
      "mean      19.243993\n",
      "std        1.688629\n",
      "min       16.000000\n",
      "25%       18.000000\n",
      "50%       18.000000\n",
      "75%       20.000000\n",
      "max       28.000000\n",
      "Name: RR (breaths/min), dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='RR (breaths/min)'>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(pcos_df['RR (breaths/min)'].describe())\n",
    "sns.boxplot(pcos_df['RR (breaths/min)'])\n",
    "\n",
    "#The normal respiration rate for an adult at rest is 12 to 20 breaths per minute. A respiration rate under 12 or over 25 breaths per minute while resting is \n",
    "#considered abnormal but it can be present so for this data I didn't do anything\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 698
    },
    "id": "JKiIvkDF5Zyu",
    "outputId": "7f338022-f3a2-46f8-c663-c5fef78ba470"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    541.000000\n",
      "mean      11.160037\n",
      "std        0.866904\n",
      "min        8.500000\n",
      "25%       10.500000\n",
      "50%       11.000000\n",
      "75%       11.700000\n",
      "max       14.800000\n",
      "Name: Hb(g/dl), dtype: float64\n",
      "count    541.000000\n",
      "mean       0.279113\n",
      "std        0.448978\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        0.000000\n",
      "75%        1.000000\n",
      "max        1.000000\n",
      "Name: Cycle(R/I), dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='Hb(g/dl)'>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#F For women, a normal level ranges between 12.3 gm/dL and 15.3 gm/dL. \n",
    "print(pcos_df['Hb(g/dl)'].describe())\n",
    "sns.boxplot(pcos_df['Hb(g/dl)'])\n",
    "\n",
    "print(pcos_df['Cycle(R/I)'].describe())\n",
    "sns.boxplot(pcos_df['Cycle(R/I)'])\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 733
    },
    "id": "9vnIFm_P57db",
    "outputId": "b1fd5557-a33e-417e-d318-4e7d3da84261"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    541.00000\n",
      "mean       4.94085\n",
      "std        1.49202\n",
      "min        0.00000\n",
      "25%        4.00000\n",
      "50%        5.00000\n",
      "75%        5.00000\n",
      "max       12.00000\n",
      "Name: Cycle length(days), dtype: float64\n",
      "Cycle length(days)\n",
      "5     276\n",
      "6      91\n",
      "4      61\n",
      "2      38\n",
      "3      36\n",
      "7      19\n",
      "9       9\n",
      "11      5\n",
      "12      2\n",
      "8       2\n",
      "0       1\n",
      "10      1\n",
      "Name: count, dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='Cycle length(days)'>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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7tvz8/IBmAmAcm80mScrJyalyf+V45XEAgp/fRSY2NlY5OTk6d+6ctm7dqgceeECSdOrUKTVq1Cig4Z577jnPrEzXrl31xBNPaPLkycrIyKj2PeHh4YqMjPTaAASHIUOGKDQ0VEuXLtXFixe99l28eFHLli1TaGiohgwZYlBCALXN7yJTeWqnbdu2stlsSkxMlHTplFPXrl0DGq60tFQhId4RQ0ND5XK5AvpzAJhDWFiYkpOTderUKSUnJ2vTpk366aeftGnTJq9xLvQF6g+/H1Ewbtw49ezZU/n5+br//vs9RSM+Pj7g18gMGjRI6enpateunTp37qyvv/5ar776akDWqAFgTpW3Vq9du1avvPKKZzw0NFSPPfYYt14D9Yzft1/v2rVL/fr1q6k8Xs6cOaPp06drw4YNOnnypGw2mx5//HHNmDHD59+4uP0aCE6s7AsEN1+/v/0uMuHh4Wrbtq1GjBih4cOH1/m7gigyAACYT42tI1NQUKAJEyZo3bp1io+PV1JSkt577z1duHDhhgIDAAD4y+8i06JFC02ePFnffPONPv/8c3Xs2FHjxo2TzWbTxIkTtX///prICQAAcBW/i8zlfv3rXys1NVUTJkzQ2bNntWzZMv3mN79Rnz59dODAgUBlBAAAqNJ1FZmKigqtW7dODz74oOLi4rRt2zYtXLhQxcXFOnr0qOLi4pScnBzorAAAAF78vtj3mWee0bvvviu3260nnnhC//Vf/6UuXbp4HVNUVCSbzVYn1nvhYl8AAMzH1+9vv9eR+f777/X6669r6NChCg8Pr/KYFi1aaNeuXf5+NAAAgF/8npExG2ZkAAAwnxqbkan0/fff6/jx41fddj148ODr/UgAAAC/+F1kfvjhB/3ud7/Tt99+K4vFosoJHYvFIklyOp2BTQgAAFANv+9a+u///m+1b99eJ0+eVOPGjXXgwAFlZ2ere/fu2r17dw1EBAAAqJrfMzI5OTnauXOnWrRooZCQEIWEhOjee+9VRkaGJk6cqK+//romcgIAAFzF7xkZp9Oppk2bSrp0d1JhYaEkKS4uTocOHQpsOgAAgGvwe0amS5cu2r9/v9q3b6+7775b8+bNU1hYmLKyshQfH18TGQEAAKrkd5FJS0vTuXPnJEmzZ8/WwIED1adPHzVv3lxr1qwJeEAAAIDqBGQdmZKSEjVr1sxz51JdwjoyAACYT42vI3O5qKioQHwMAACAX3wqMkOHDvX5A9evX3/dYQDAV7m5uZo4caLn9WuvvaaEhAQDEwEwgk9Fxmq1ev7sdru1YcMGWa1Wde/eXZL01Vdf6fTp034VHgC4XomJiVeNVZYa1rMC6hefisybb77p+fPUqVM1bNgwLV68WKGhoZIu3ZI9btw4rkEBUOOuLDFJSUnatm2b137KDFB/+H2xb8uWLfXJJ5/otttu8xo/dOiQevfurZ9//jmgAW8UF/sCwePy00lZWVnq2LGjZ9/hw4c1ZswYSZxmAoKBr9/ffi+Id/HiRR08ePCq8YMHD8rlcvn7cQDgs8uvibm8xFz5+vLjAAQ3v+9aGjFihEaNGqV//vOf6tmzpyTp888/10svvaQRI0YEPCAAXCkpKanK8d/+9rfauXNnLacBYCS/i8z8+fPVunVrvfLKKzpx4oQkKSYmRs8995ymTJkS8IAAcKVt27YpNTX1qnFKDFD/+H1qKSQkRM8//7wKCgp0+vRpnT59WgUFBXr++ec9F/8CQE147bXXPH8+fPiw177LX19+HIDgdkML4nHxLIDadPkFvJUX9lZ1OokLfYH6w6cZmX//93/Xnj17fvG4M2fOaO7cucrMzLzhYABQlStvrb6yxHDrNVC/+DQjk5ycrEceeURWq1WDBg1S9+7dZbPZ1KhRI506dUrff/+9PvnkE23ZskUPPfSQXn755ZrODaAe2717Nyv7ApDkxzoy5eXlWrt2rdasWaNPPvlEdrv90gdYLLrjjjuUlJSkUaNG6fbbb6/RwP5iHRkAAMzH1+/v6376td1uV1lZmZo3b66GDRted9CaRpEBAMB8avzp11ar1esZTAAAALXtuorMkSNHtGvXLp08efKq1XxnzJgRkGAAAAC/xO8i89e//lVjx45VixYt1Lp1a1ksFs8+i8VCkQEAALXG7yIzZ84cpaena+rUqTWRBwAAwGd+r+x76tQpJScn10QWAAAAv/hdZJKTk/W3v/2tJrIAAAD4xadTS5c/t+TWW2/V9OnTtWfPHnXt2vWqW68vX6AKAACgJvm0jkz79u19+zCLRT/88MMNhwok1pEBAMB8ArqOTF5eXsCCAQAABIrf18jMnj1bpaWlV42XlZVp9uzZAQkFAADgC78fURAaGqoTJ06oVatWXuM///yzWrVqJafTGdCAN4pTSwAAmI+v399+z8i43W6vRfAq7d+/X1FRUf5+HAAAwHXzeUG8Zs2ayWKxyGKxqGPHjl5lxul06uzZs3r66adrJCQAAEBVfC4yCxYskNvt1siRIzVr1iyvB0aGhYXp5ptvVq9evWokJAAAQFV8LjLDhw+XdOlW7N69e1+1fgwAAEBt8/tZS3fddZfKyspUVlbmNW6xWBQeHq6wsLCAhQMAALgWvy/2vemmm9SsWbOrtptuukkRERGKi4vTzJkz5XK5AhKwoKBA//mf/6nmzZsrIiJCXbt21ZdffhmQzwYAAObm94zM8uXLNW3aNP3hD39Qz549JUlffPGFVqxYobS0NP3444+aP3++wsPD9eKLL95QuFOnTumee+5Rv3799OGHH6ply5Y6cuSImjVrdkOfCwAAgoPf68jcd999euqppzRs2DCv8ffee09LlizRjh079Pbbbys9PV0HDx68oXAvvPCCPv30U3388cfX/RmsIwMAgPn4+v3td5GJiIhQbm6uOnTo4DV+5MgRdevWTaWlpcrLy1Pnzp2rXAHYH3fccYeSkpL0f//3f/roo4/Upk0bjRs3TqNHj672PeXl5SovL/e8djgcio2NpcggYM6fP6/jx48bHQOok9q1a6dGjRoZHQNBIKDPWrpcbGysli5dqpdeeslrfOnSpYqNjZV0aZXfQJz++eGHH7Ro0SL98Y9/1Isvvqi9e/dq4sSJCgsL89xFdaWMjAzNmjXrhn82UJ3jx49rzJgxRscA6qSsrCx17NjR6BioR/yekfnf//1fJScnq1OnTurRo4ck6csvv9TBgwe1bt06DRw4UIsWLdKRI0f06quv3lC4sLAwde/eXZ999plnbOLEidq7d69ycnKqfA8zMqhpzMjUHceOHVN6erqmTZumuLg4o+NAzMggcGpsRmbw4ME6ePCglixZosOHD0uSBgwYoA8++EA333yzJGns2LHXl/oKMTExuuOOO7zGbr/9dr3//vvVvic8PFzh4eEB+flAVRo1asRvnHVMXFwc/02AesrvIiNdWhTvylNLNeGee+7RoUOHvMYOHz7Mb14AAEDSdRaZ06dP64svvtDJkyevWi/mySefDEgwSZo8ebJ69+6tv/zlLxo2bJi++OILZWVlKSsrK2A/AwAAmJffRWbTpk1KSUnR2bNnFRkZ6fXwSIvFEtAi06NHD23YsEGpqamaPXu22rdvrwULFiglJSVgPwMAAJiX30VmypQpGjlypP7yl7+ocePGNZHJy8CBAzVw4MAa/zkAAMB8/H5EQUFBgSZOnFgrJQYAAOBa/C4ySUlJPOsIAADUCX6fWnrooYf03HPP6fvvv1fXrl3VsGFDr/2DBw8OWDgAAIBr8bvIVD4eYPbs2Vfts1gscjqdN54KAADAB34XmStvtwYAADCK39fIXO78+fOBygEAAOA3v4uM0+nUn//8Z7Vp00a/+tWv9MMPP0iSpk+frqVLlwY8IAAAQHX8LjLp6elavny55s2bp7CwMM94ly5d9D//8z8BDQcAAHAtfheZt956S1lZWUpJSVFoaKhnvFu3bjp48GBAwwEAAFzLdS2Id+utt1417nK5VFFREZBQAAAAvvC7yNxxxx36+OOPrxpft26d7rrrroCEAgAA8IXft1/PmDFDw4cPV0FBgVwul9avX69Dhw7prbfe0ubNm2siIwAAQJX8npEZMmSINm3apL///e9q0qSJZsyYoX/84x/atGmT7r///prICAAAUCW/Z2QkqU+fPtq+fXugswAAAPjlhhbEAwAAMJJPMzLNmjWTxWLx6QNLSkpuKBAAAICvfCoyCxYsqOEYAAAA/vOpyAwfPrymcwAAAPiNa2QAAIBpUWQAAIBpXdft1zDGwYMHlZ+fb3QMoM44ceKEJGnPnj06duyYwWmAuiM2NladOnUyOkatoMiYRHFxscaNGy+Xy2l0FKDOWbZsmdERgDolJCRU7777jqKjo42OUuOuu8gcPXpU//znP9W3b19FRETI7Xb7fIs2/Ge32+VyOXW+za/lDvuV0XEAAHWU5cJZNSrYJ7vdTpGpys8//6xHH31UO3fulMVi0ZEjRxQfH69Ro0apWbNmeuWVV2oiJ/4/p7WtXE1aGB0DAFBHhZz7SSrYZ3SMWuP3xb6TJ09WgwYNdPz4cTVu3Ngz/uijj2rr1q0BDQcAAHAtfs/I/O1vf9O2bdvUtm1br/EOHTpwsR0AAKhVfs/InDt3zmsmplJJSYnCw8MDEgoAAMAXfheZPn366K233vK8tlgscrlcmjdvnvr16xfQcAAAANfi96mlefPm6b777tOXX36pCxcu6Pnnn9eBAwdUUlKiTz/9tCYyAgAAVMnvGZkuXbro8OHDuvfeezVkyBCdO3dOQ4cO1ddff61bbrmlJjICAABU6brWkbFarZo2bVqgswAAAPjFpyKTm5vr8wcmJCRcdxgAAAB/+FRk7rzzTlksFrnd7mseZ7FY5HSyhD4AAKgdPhWZvLy8ms4BAADgN5+KTFxcXE3nAAAA8Jvfdy1lZGRU+aTZZcuWae7cuQEJBQAA4Au/i8ySJUvUqVOnq8Y7d+6sxYsXByQUAACAL/wuMkVFRYqJiblqvGXLljpx4kRAQgEAAPjC7yITGxtb5Qq+n376qWw2W0BCAQAA+MLvBfFGjx6tSZMmqaKiQr/97W8lSTt27NDzzz+vKVOmBDwgAABAdfwuMs8995x+/vlnjRs3ThcuXJAkNWrUSFOnTlVqamrAAwIAAFTH7yJjsVg0d+5cTZ8+Xf/4xz8UERGhDh06KDw8vCbyAQAAVMvva2TefPNNlZWV6Ve/+pV69OihLl26UGIAAIAh/C4yL7zwgqKjozVq1Ch99tlnNZEJAADAJ34XmYKCAq1YsUI//fSTEhMT1alTJ82dO1dFRUU1kQ8AAKBafheZBg0a6He/+502btyo/Px8jR49WqtWrVK7du00ePBgbdy4US6Xqyay6qWXXpLFYtGkSZNq5PMBAIC5+F1kLhcdHa17771XvXr1UkhIiL799lsNHz5ct9xyi3bv3h2giJfs3btXS5YsUUJCQkA/FwAAmNd1FZni4mLNnz9fnTt3VmJiohwOhzZv3qy8vDwVFBRo2LBhGj58eMBCnj17VikpKfrrX/+qZs2aBexzAQCAufl9+/WgQYO0bds2dezYUaNHj9aTTz6pqKgoz/4mTZpoypQpevnllwMWcvz48XrooYfUv39/zZkz55rHlpeXq7y83PPa4XAELEddEHLebnQEAEAdVt++J/wuMq1atdJHH32kXr16VXtMy5YtlZeXd0PBKq1evVr79u3T3r17fTo+IyNDs2bNCsjPrkusVqsahoVLP3xkdBQAQB3XMCxcVqvV6Bi1wuJ2u91Gh6hOfn6+unfvru3bt3uujUlMTNSdd96pBQsWVPmeqmZkYmNjZbfbFRkZWRuxa0xxcbHs9vrVtIFrOXbsmNLT0zVt2jTFxcUZHQeoM6xWq6Kjo42OcUMcDoesVusvfn/7PCOzc+dOTZgwQXv27LnqA+12u3r37q3FixerT58+15/6Cl999ZVOnjypX//6154xp9Op7OxsLVy4UOXl5QoNDfV6T3h4eNAu0BcdHW36/zGBmhAXF6eOHTsaHQOAAXwuMgsWLNDo0aOrbEVWq1VPPfWUXn311YAWmfvuu0/ffvut19iIESPUqVMnTZ069aoSAwAA6hefi8z+/fs1d+7cavc/8MADmj9/fkBCVWratKm6dOniNdakSRM1b978qnEAAFD/+Hz7dXFxsRo2bFjt/gYNGujHH38MSCgAAABf+Dwj06ZNG3333Xe69dZbq9yfm5urmJiYgAWrTqAX2gMAAObl84zMgw8+qOnTp+v8+fNX7SsrK9PMmTM1cODAgIYDAAC4Fp9nZNLS0rR+/Xp17NhREyZM0G233SZJOnjwoDIzM+V0OjVt2rQaCwoAAHAln4tMdHS0PvvsM40dO1apqamqXH7GYrEoKSlJmZmZ3BoMAABqlV8r+8bFxWnLli06deqUjh49KrfbrQ4dOvD8IwAAYAi/H1EgSc2aNVOPHj0CnQUAAMAv1/X0awAAgLqAIgMAAEyLIgMAAEyLIgMAAEyLIgMAAEyLIgMAAEyLIgMAAEyLIgMAAEyLIgMAAEyLIgMAAEyLIgMAAEyLIgMAAEyLIgMAAEyLIgMAAEyLIgMAAEyrgdEBALM5f/68jh8/bnQMSDp27JjXP2G8du3aqVGjRkbHQD1CkQH8dPz4cY0ZM8boGLhMenq60RHw/2VlZaljx45Gx0A9QpEB/NSuXTtlZWUZHQOok9q1a2d0BNQzFBnAT40aNeI3TgCoI7jYFwAAmBZFBgAAmBZFBgAAmBZFBgAAmBZFBgAAmBZFBgAAmBZFBgAAmBZFBgAAmBZFBgAAmBZFBgAAmBZFBgAAmBZFBgAAmBZFBgAAmBZFBgAAmBZFBgAAmBZFBgAAmBZFBgAAmBZFBgAAmBZFBgAAmBZFBgAAmBZFBoAprVu3TomJiZ5t3bp1RkcCYIA6XWQyMjLUo0cPNW3aVK1atdLDDz+sQ4cOGR0LgMESExO1cOFCr7GFCxcqMTHRmEAADFOni8xHH32k8ePHa8+ePdq+fbsqKir0wAMP6Ny5c0ZHA2CQK8tKq1atrrkfQHBrYHSAa9m6davX6+XLl6tVq1b66quv1LdvX4NSATDK5aePUlNTlZSU5Hm9bds2ZWRkeI77/e9/X+v5ANS+Oj0jcyW73S5JioqKqvaY8vJyORwOrw1AcLj8dNLlJebK11eedgIQvExTZFwulyZNmqR77rlHXbp0qfa4jIwMWa1WzxYbG1uLKQHUhitPJ1W61i85AIKTaYrM+PHj9d1332n16tXXPC41NVV2u92z5efn11JCALXl5MmTVY6XlJTUchIARjNFkZkwYYI2b96sXbt2qW3bttc8Njw8XJGRkV4bgOAwYcIEz5+3bdvmte/y15cfByC4Wdxut9voENVxu9165plntGHDBu3evVsdOnTw+zMcDoesVqvsdjulBggCV96VFBUVddVMzO7du2svEIAa4ev3d52ekRk/frxWrlypd955R02bNlVRUZGKiopUVlZmdDQABrmypFBigPqtTheZRYsWyW63KzExUTExMZ5tzZo1RkcDYKDdu3dfdfpowoQJlBigHqrTp5YCgVNLAACYT1CcWgIAALgWigwAADAtigwAADAtigwAADAtigwAADAtigwAADAtigwAADAtigwAADAtigwAADAtigwAADAtigwAADAtigwAADAtigwAADAtigwAADAtigwAADAtigwAADAtigwAADAtigwAADAtigwAADAtigwAADAtigwAADAtigwAADAtigwAADAtigwAADAtigwAADAtigwAADAtigwAADAtigwAADAtigwAADAtigwAADAtigwAADAtigwAADAtigwAADAtigwAADAtigwAADAtigwAADAtigwAADAtigwAADAtigwAADAtigwAADAtigwAADAtigwAADAtigwAADAtigwAADCtBkYHAIDr4XQ6lZubq5KSEkVFRSkhIUGhoaFGxwJQy0xRZDIzM/Xyyy+rqKhI3bp10+uvv66ePXsaHQuAQbKzs/XGG2+oqKjIM9a6dWuNGzdOffv2NTAZgNpW508trVmzRn/84x81c+ZM7du3T926dVNSUpJOnjxpdDQABsjOztbMmTMVHx+vzMxMbdmyRZmZmYqPj9fMmTOVnZ1tdEQAtcjidrvdRoe4lrvvvls9evTQwoULJUkul0uxsbF65pln9MILL/zi+x0Oh6xWq+x2uyIjI2s6LoAa5HQ6lZKSovj4eM2ZM0chIf/6XczlciktLU15eXlauXIlp5kAk/P1+7tOz8hcuHBBX331lfr37+8ZCwkJUf/+/ZWTk1Ple8rLy+VwOLw2AMEhNzdXRUVFSklJ8Sox0qW/G1JSUnTixAnl5uYalBBAbavTReann36S0+lUdHS013h0dLTXufHLZWRkyGq1erbY2NjaiAqgFpSUlEiS2rdvX+X+yvHK4wAEvzpdZK5Hamqq7Ha7Z8vPzzc6EoAAiYqKkiTl5eVVub9yvPI4AMGvTheZFi1aKDQ0VMXFxV7jxcXFat26dZXvCQ8PV2RkpNcGIDgkJCSodevWWrVqlVwul9c+l8ulVatWKSYmRgkJCQYlBFDb6nSRCQsL029+8xvt2LHDM+ZyubRjxw716tXLwGQAjBAaGqpx48YpJydHaWlpOnDggEpLS3XgwAGlpaUpJydHY8eO5UJfoB6p83ctrVmzRsOHD9eSJUvUs2dPLViwQO+9954OHjx41bUzVeGuJSD4VLWOTExMjMaOHcs6MkCQ8PX7u84viPfoo4/qxx9/1IwZM1RUVKQ777xTW7du9anEAAhOffv21T333MPKvgDq/ozMjWJGBgAA8wmKdWQAAACuhSIDAABMiyIDAABMiyIDAABMiyIDAABMiyIDAABMiyIDAABMiyIDAABMiyIDAABMq84/ouBGVS5c7HA4DE4CAAB8Vfm9/UsPIAj6InPmzBlJUmxsrMFJAACAv86cOSOr1Vrt/qB/1pLL5VJhYaGaNm0qi8VidBwAAeRwOBQbG6v8/HyepQYEGbfbrTNnzshmsykkpPorYYK+yAAIXjwUFgAX+wIAANOiyAAAANOiyAAwrfDwcM2cOVPh4eFGRwFgEK6RAQAApsWMDAAAMC2KDAAAMC2KDAAAMC2KDAAAMC2KDAAAMC2KDAAAMC2KDAAAMC2KDAAAMK3/B1nx8wbQTsd0AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(pcos_df['Cycle length(days)'].describe())\n",
    "print(pcos_df['Cycle length(days)'].value_counts())\n",
    "sns.boxplot(pcos_df['Cycle length(days)'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 716
    },
    "id": "3Ne853VD64GL",
    "outputId": "cd11f731-9ad3-43a4-f176-a9a93888f3ef"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    541.000000\n",
      "mean       4.950092\n",
      "std        1.476766\n",
      "min        2.000000\n",
      "25%        5.000000\n",
      "50%        5.000000\n",
      "75%        5.000000\n",
      "max       12.000000\n",
      "Name: Cycle length(days), dtype: float64\n",
      "Cycle length(days)\n",
      "5     277\n",
      "6      91\n",
      "4      61\n",
      "2      38\n",
      "3      36\n",
      "7      19\n",
      "9       9\n",
      "11      5\n",
      "8       2\n",
      "12      2\n",
      "10      1\n",
      "Name: count, dtype: int64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\User\\AppData\\Local\\Temp\\ipykernel_19708\\3605193208.py:3: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
      "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
      "\n",
      "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
      "\n",
      "\n",
      "  pcos_df['Cycle length(days)'].mask(pcos_df['Cycle length(days)'] == 0 ,pcos_df['Cycle length(days)'].median() , inplace=True)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='Cycle length(days)'>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#since cycle length cannot be 0 replacing that with median\n",
    "\n",
    "pcos_df['Cycle length(days)'].mask(pcos_df['Cycle length(days)'] == 0 ,pcos_df['Cycle length(days)'].median() , inplace=True)\n",
    "\n",
    "print(pcos_df['Cycle length(days)'].describe())\n",
    "print(pcos_df['Cycle length(days)'].value_counts())\n",
    "sns.boxplot(pcos_df['Cycle length(days)'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Tgl8rIWN9G4s",
    "outputId": "5822d3cd-1f19-4c94-eba0-aa031f2e04b1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count      541.000000\n",
      "mean       664.549235\n",
      "std       3348.920576\n",
      "min          1.300000\n",
      "25%          1.990000\n",
      "50%         20.000000\n",
      "75%        297.210000\n",
      "max      32460.970000\n",
      "Name:   I   beta-HCG(mIU/mL), dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(pcos_df['  I   beta-HCG(mIU/mL)'].describe())\n",
    "\n",
    "#skipped   I   beta-HCG(mIU/mL) and II beta-HCG(mIU/mL) refer to google docs for the level they should be at"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 507
    },
    "id": "EJWnzCgS9MB7",
    "outputId": "8d232dee-edc9-4d5c-83e4-9f30f2efcb14"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count     541.000000\n",
      "mean       14.601832\n",
      "std       217.022081\n",
      "min         0.210000\n",
      "25%         3.300000\n",
      "50%         4.850000\n",
      "75%         6.410000\n",
      "max      5052.000000\n",
      "Name: FSH(mIU/mL), dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='FSH(mIU/mL)'>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(pcos_df['FSH(mIU/mL)'].describe())\n",
    "\n",
    "\n",
    "sns.boxplot(pcos_df['FSH(mIU/mL)'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 507
    },
    "id": "819V3co4D_w6",
    "outputId": "df56561b-92b8-4f42-d3b2-02c6ffdd0e4a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    541.000000\n",
      "mean       5.272534\n",
      "std        4.485269\n",
      "min        0.210000\n",
      "25%        3.300000\n",
      "50%        4.850000\n",
      "75%        6.400000\n",
      "max       65.400000\n",
      "Name: FSH(mIU/mL), dtype: float64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\User\\AppData\\Local\\Temp\\ipykernel_19708\\2208924748.py:3: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
      "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
      "\n",
      "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
      "\n",
      "\n",
      "  pcos_df['FSH(mIU/mL)'].mask(pcos_df['FSH(mIU/mL)'] > 134.80 ,pcos_df['FSH(mIU/mL)'].median() , inplace=True)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='FSH(mIU/mL)'>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Follicle-stimulating hormone (FSH) : During puberty: 0.3 to 10.0 mIU/mL Adult: 1.5 to 12.4 mIU/mL \n",
    "#Removing values greater than 134.80\n",
    "pcos_df['FSH(mIU/mL)'].mask(pcos_df['FSH(mIU/mL)'] > 134.80 ,pcos_df['FSH(mIU/mL)'].median() , inplace=True)\n",
    "print(pcos_df['FSH(mIU/mL)'].describe())\n",
    "\n",
    "\n",
    "sns.boxplot(pcos_df['FSH(mIU/mL)'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 507
    },
    "id": "otdihesFEv4B",
    "outputId": "3f6a35d2-b9c8-4433-be82-6c89b4127ab0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count     541.000000\n",
      "mean        6.469919\n",
      "std        86.673255\n",
      "min         0.020000\n",
      "25%         1.020000\n",
      "50%         2.300000\n",
      "75%         3.680000\n",
      "max      2018.000000\n",
      "Name: LH(mIU/mL), dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='LH(mIU/mL)'>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Luteinizing Hormone (LH):\n",
    "\n",
    "print(pcos_df['LH(mIU/mL)'].describe())\n",
    "\n",
    "\n",
    "sns.boxplot(pcos_df['LH(mIU/mL)'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 507
    },
    "id": "qCIgY9_5Fatc",
    "outputId": "f7d7426a-c3ee-4937-ff2a-c87b844524dc"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    541.000000\n",
      "mean       2.744041\n",
      "std        2.305620\n",
      "min        0.020000\n",
      "25%        1.020000\n",
      "50%        2.300000\n",
      "75%        3.670000\n",
      "max       14.690000\n",
      "Name: LH(mIU/mL), dtype: float64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\User\\AppData\\Local\\Temp\\ipykernel_19708\\1560931975.py:2: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
      "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
      "\n",
      "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
      "\n",
      "\n",
      "  pcos_df['LH(mIU/mL)'].mask(pcos_df['LH(mIU/mL)'] > 101 ,pcos_df['LH(mIU/mL)'].median() , inplace=True)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='LH(mIU/mL)'>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Removing values greater than 101\n",
    "pcos_df['LH(mIU/mL)'].mask(pcos_df['LH(mIU/mL)'] > 101 ,pcos_df['LH(mIU/mL)'].median() , inplace=True)\n",
    "print(pcos_df['LH(mIU/mL)'].describe())\n",
    "\n",
    "\n",
    "sns.boxplot(pcos_df['LH(mIU/mL)'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 507
    },
    "id": "Bc2MzepJUb69",
    "outputId": "4f5fff02-1db7-4235-c4cd-544d5efcdfe8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count     541.000000\n",
      "mean        6.904917\n",
      "std        60.691982\n",
      "min         0.000000\n",
      "25%         1.420000\n",
      "50%         2.170000\n",
      "75%         3.960000\n",
      "max      1372.830000\n",
      "Name: FSH/LH, dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='FSH/LH'>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "print(pcos_df['FSH/LH'].describe())\n",
    "\n",
    "\n",
    "sns.boxplot(pcos_df['FSH/LH'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 507
    },
    "id": "uA50wX99GPnD",
    "outputId": "8378b97f-b1f5-434f-a15e-eeff97df1862"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    541.000000\n",
      "mean       4.373240\n",
      "std       14.900231\n",
      "min        0.228261\n",
      "25%        1.416244\n",
      "50%        2.161172\n",
      "75%        3.939394\n",
      "max      327.000000\n",
      "Name: FSH/LH, dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='FSH/LH'>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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1O6Lm2bEKYQcAgIFnQM6zAwAAEG6EHQAAYDTCDgAAMBphBwAAGI2wAwAAjEbYAQAARiPsAAAAoxF2AACA0Qg7AADAaIQdAABgtF5/CHTTpk096nfzzTf3uhgAAIBw63XYmTNnTsi6zWbTmZ/Xstls6uzsPKvCAAAAwqHXj7H8fn/IMmzYMB09ejRkG0EHAABECsbsAAAAoxF2AACA0Qg7AADAaGcddmw2m2w2WzhqAQAACLtev431ox/9KCTcnDx5UpMmTVJUVGhuam5uPvvqAAAAzlKvw84zzzzTB2UAAAD0jV6Hnfz8/O9s7+joUFNT0w8uCAAAIJzCPkC5pqZGaWlp4d4tAADAD8LbWAAAwGiEHQAAYDTCDgAAMFqvByhXV1d/Z/vhw4d/cDEAAADh1uuwM3HixG6/dC797wvoTDIIAAAiRa/DTm1tbV/UAQAA0Cd6HXYqKip0880367zzzuuLegAAAMKq1wOU169fr1GjRuknP/mJHn/8cX300Ud9URcAAEBY9Drs7NixQw0NDVq6dKkOHDigrKwsXXjhhbr33ntVWVkpv9/fF3UCAAD8ILZAdyONe6GtrU07duzQpk2btHnzZv33v//VzJkzdfPNN2vGjBmKj48PV619xuv1yuFwyOPxyG63W10OAADogZ5ev8867JzpnXfe0aZNm/T6669r/vz5evDBB8O5+z5B2AEAYOCxLOx8XXt7u4YMGdJXuw8bwg4AAANPT6/fvX4ba8WKFd/bx2az6Y9//OOACDoAAMBsvQ477733Xsj622+/rcmTJ2vo0KHBbUwqCAAAIkWvw85bb70Vsj5ixAiVl5crPT09bEUBAACECx8CBQAARiPsAAAAoxF2AACA0Xo9Zqe6ujpkPRAI6NChQzp58mTI9szMzLOrDAAAIAx6fWdn4sSJmjRpkiZOnKiJEyfq1KlTuummm0K2T5o0qUf7qqys1OzZs5WamiqbzabXXnstpH3RokWy2Wwhy/Tp00P6NDc3Ky8vT3a7XQkJCbrjjju+EbwAAMDg1es7O7W1tWE7eGtrqyZMmKBf/vKXmjt3brd9pk+frrVr1wbX4+LiQtrz8vLU0NCg7du3q729XYsXL9aSJUtUXl4etjoBAMDA1euwM2bMmLAdfMaMGZoxY8Z39omLi5PT6ey27aOPPtK2bdu0f/9+XXHFFZKkZ599VjNnztRTTz2l1NTUsNUKAAAGpl4/xvriiy/02WefhWyrqanR4sWL9bOf/Szsd1R27typpKQkXXzxxSooKNCXX34ZbHO5XEpISAgGHUnKyclRVFSU9u7d+6379Pl88nq9IQsAADBTr8PO3XffrVWrVgXXm5qadO2112r//v3y+XxatGiRXnrppbAUN336dL344ouqqKjQ448/rl27dmnGjBnq7OyUJLndbiUlJYX8JiYmRomJiXK73d+639LSUjkcjuCSlpYWlnoBAEDk6fVjrD179mjdunXB9RdffFGJiYmqqqpSTEyMnnrqKZWVlWnhwoVnXdyCBQuCf48fP16ZmZk6//zztXPnTt1www0/eL9FRUUh3/jyer0EHgAADNXrOztut1s//vGPg+s7duzQ3LlzFRPzVW66+eab9fHHH4etwK9LT0/Xeeedp6NHj0qSnE6nmpqaQvp0dHSoubn5W8f5SF+NA7Lb7SELAAAwU6/Djt1uV0tLS3B93759ysrKCq7bbDb5fL6wFHemzz//XF9++aVSUlIkSdnZ2WppadGBAweCfXbs2CG/3x9SEwAAGLx6HXamTJmiVatWye/365///KdOnDih66+/Pth+5MiRHj8SOnnypKqqqlRVVSXpq9faq6qqVFdXp5MnT+r+++/Xnj179Omnn6qiokK33HKLLrjgAuXm5kqSxo0bp+nTp+vOO+/Uvn379O9//1vLli3TggULeBMLAABIkmyBQCDQmx+8//77ysnJkdfrVUdHh4qKivToo48G2xcuXKj4+HitWbPme/e1c+dOXXfddd/Ynp+fr+eee05z5szRe++9p5aWFqWmpmratGn63e9+p+Tk5GDf5uZmLVu2TJs3b1ZUVJTmzZunVatWafjw4T0+J6/XK4fDIY/HwyMtAAAGiJ5ev3sddv7zn/9o+PDhcrlccjqd33hc9K9//UsZGRkaO3bsD6vcAoQdAAAGnj4LO9HR0WpoaAi+8n3rrbdq1apVIXdbBhrCDgAAA09Pr9+9HrNzZjbasmWLWltbe18hAABAP+h12AEAABhIeh12ur4+fuY2AACASNTrGZQDgYAWLVoU/Pr46dOndddddyk+Pj6k36uvvhqeCgEAAM5Cr8NOfn5+yPptt90WtmIAAADCrddhZ+3atX1RBwAAQJ9ggDIAADAaYQcAABiNsAMAAIxG2AEAAEYj7AAAAKMRdgAAgNEIOwAAwGiEHQAAYDTCDgAAMBphBwAAGI2wAwAAjEbYAQAARiPsAAAAoxF2AACA0Qg7AADAaIQdAABgNMIOAAAwGmEHAAAYjbADAACMRtgBAABGI+wAAACjEXYAAIDRCDsAAMBohB0AAGA0wg4AADAaYQcAABiNsAMAAIxG2AEAAEYj7AAAAKMRdgAAgNEIOwAAwGiEHQAAYDTCDgAAMBphBwAAGM3SsFNZWanZs2crNTVVNptNr732Wkh7IBDQQw89pJSUFA0dOlQ5OTn6+OOPQ/o0NzcrLy9PdrtdCQkJuuOOO3Ty5Ml+PAsAABDJLA07ra2tmjBhgsrKyrptf+KJJ7Rq1SqtWbNGe/fuVXx8vHJzc3X69Olgn7y8PNXU1Gj79u164403VFlZqSVLlvTXKQAAgAhnCwQCAauLkCSbzaaNGzdqzpw5kr66q5Oamqp7771X9913nyTJ4/EoOTlZ69at04IFC/TRRx8pIyND+/fv1xVXXCFJ2rZtm2bOnKnPP/9cqampPTq21+uVw+GQx+OR3W7vk/MDAADh1dPrd8SO2amtrZXb7VZOTk5wm8PhUFZWllwulyTJ5XIpISEhGHQkKScnR1FRUdq7d++37tvn88nr9YYsAADATBEbdtxutyQpOTk5ZHtycnKwze12KykpKaQ9JiZGiYmJwT7dKS0tlcPhCC5paWlhrh4AAESKiA07famoqEgejye4HDt2zOqSAABAH4nYsON0OiVJjY2NIdsbGxuDbU6nU01NTSHtHR0dam5uDvbpTlxcnOx2e8gCAADMFLFhZ+zYsXI6naqoqAhu83q92rt3r7KzsyVJ2dnZamlp0YEDB4J9duzYIb/fr6ysrH6vGQAARJ4YKw9+8uRJHT16NLheW1urqqoqJSYmavTo0brnnnv06KOP6sILL9TYsWP14IMPKjU1NfjG1rhx4zR9+nTdeeedWrNmjdrb27Vs2TItWLCgx29iAQAAs1kadt555x1dd911wfUVK1ZIkvLz87Vu3Tr9+te/Vmtrq5YsWaKWlhZdc8012rZtm84555zgbzZs2KBly5bphhtuUFRUlObNm6dVq1b1+7kAAIDIFDHz7FiJeXYAABh4Bvw8OwAAAOFA2AEAAEYj7AAAAKMRdgAAgNEIOwAAwGiEHQAAYDTCDgAAMBphBwAAGI2wAwAAjEbYAQAARiPsAAAAoxF2AACA0Qg7AADAaIQdAABgNMIOAAAwGmEHAAAYjbADAACMRtgBAABGI+wAAACjEXYAAIDRCDsAAMBohB0AAGA0wg4AADAaYQcAABiNsAMAAIxG2AEAAEYj7AAAAKPFWF0AAPSVzs5OVVdXq7m5WYmJicrMzFR0dLTVZQHoZ4QdAEaqrKzU6tWr5Xa7g9ucTqeWLl2qqVOnWlgZgP7GYywAxqmsrFRJSYnS09NVVlamLVu2qKysTOnp6SopKVFlZaXVJQLoR7ZAIBCwugireb1eORwOeTwe2e12q8sBcBY6OzuVl5en9PR0Pfroo4qK+t9/0/n9fhUXF6u2tlbr16/nkRYwwPX0+s2dHQBGqa6ultvtVl5eXkjQkaSoqCjl5eWpoaFB1dXVFlUIoL8RdgAYpbm5WZI0duzYbtu7tnf1A2A+wg4AoyQmJkqSamtru23v2t7VD4D5CDsAjJKZmSmn06kNGzbI7/eHtPn9fm3YsEEpKSnKzMy0qEIA/Y2wA8Ao0dHRWrp0qVwul4qLi1VTU6NTp06ppqZGxcXFcrlcKigoYHAyMIjwNpZ4GwswUXfz7KSkpKigoIB5dgBD9PT6TdgRYQcwFTMoA2br6fWbGZQBGCs6OlqTJk2yugwAFiPsADAWd3YASBE+QPnhhx+WzWYLWS655JJg++nTp1VYWKhzzz1Xw4cP17x589TY2GhhxQAiRWVlpfLy8rR8+XL97ne/0/Lly5WXl8enIoBBKKLDjiRdeumlamhoCC5vv/12sG358uXavHmzXnnlFe3atUv19fWaO3euhdUCiAR8GwvA10X0AOWHH35Yr732mqqqqr7R5vF4NHLkSJWXl2v+/PmSpEOHDmncuHFyuVyaMmVKj4/DAGXAHHwbCxg8jPk21scff6zU1FSlp6crLy9PdXV1kqQDBw6ovb1dOTk5wb6XXHKJRo8eLZfL9Z379Pl88nq9IQsAM/BtLABniuiwk5WVpXXr1mnbtm167rnnVFtbq2uvvVYnTpyQ2+1WbGysEhISQn6TnJwcMq9Gd0pLS+VwOIJLWlpaH54FgP7Et7EAnCmi38aaMWNG8O/MzExlZWVpzJgx+sc//qGhQ4f+4P0WFRVpxYoVwXWv10vgAQzx9W9jXXrppd9o59tYwOAT0Xd2zpSQkKCLLrpIR48eldPpVFtbm1paWkL6NDY2yul0fud+4uLiZLfbQxYAZuDbWADONKDCzsmTJ/XJJ58oJSVFkydP1pAhQ1RRURFsP3z4sOrq6pSdnW1hlQCsxLexAJwpot/Guu+++zR79myNGTNG9fX1KikpUVVVlT788EONHDlSBQUF2rJli9atWye73a67775bkrR79+5eHYe3sQDz8G0swHxGfC7i888/189//nN9+eWXGjlypK655hrt2bNHI0eOlCQ9/fTTioqK0rx58+Tz+ZSbm6vVq1dbXDWASDB16lRdffXVzKAMILLv7PQX7uwAADDwGDPPDgAAwNkg7AAAAKMRdgAAgNEIOwAAwGgR/TYWAJyNzs5O3sYCQNgBYKbu5tlxOp1aunQp8+wAgwxhB4BxKisrVVJSoilTpujWW29VXFycfD6f9u3bp5KSEq1cuZLAAwwizLMj5tkBTNLZ2am8vLzgv9Nn3tlxOBzyer1av349j7SAAY55dgAMStXV1XK73Tpy5IjS09NVVlamLVu2qKysTOnp6Tpy5IgaGhpUXV1tdakA+glhB4BRvvjiC0nSVVddpZUrV6qtrU0ul0ttbW1auXKlrrrqqpB+AMzHmB0ARmlpaZEkJScn67bbblNjY2OwLTk5WVlZWSH9AJiPsAPAKAkJCZKkTZs2faOtsbExuL2rHwDz8RgLgFESExPD2g/AwEfYAWCUjo6O4N9DhgwJaYuNje22HwCz8RgLgFG2b98e/Hvy5MnKysoKzrOzd+9e7dmzJ9iva/wOALMRdgAYpWtendzcXL3//vvBcCNJKSkpuvHGG7V9+/aQ+XcAmI2wA8AoTqdTBw8e1JEjR/TSSy/p4MGDwW9jXXbZZVqyZEmwH4DBgTE7AIySm5srSaqtrdWDDz6oTz/9VD6fT59++qkefPBB1dbWhvQDYD7u7AAwyuWXX674+Hi1trZqz549IY+xusTHx+vyyy+3oDoAVuDODgCjREdHa/bs2d/ZZ/bs2XwXCxhECDsAjNLZ2alt27Z9Z59t27aps7OznyoCYDXCDgCjVFVVBT8F8fV5db6+3tLSoqqqqn6uDIBVCDsAjPLuu+8G/25rawtp+/r61/sBMBthB4BRvv7hz3D0AzDwEXYAGKWnY3EYswMMHoQdAEb5/PPPw9oPwMBH2AFglFOnToW1H4CBj7ADwCiBQCCs/QAMfIQdAEZhzA6AMxF2ABilvb09rP0ADHyEHQBGIewAOBNhB4BRbDZbWPsBGPgIOwCMwgBlAGci7AAwit/vD2s/AAMfYQeAUU6fPh3WfgAGPsIOAKPw6jmAM8VYXQBgmtOnT6uurs7qMtADR44csbqEQWn06NE655xzrC4DgwhhBwizuro6LVmyxOoy0AP8c7LG888/r4suusjqMjCIEHaAMBs9erSef/55q8sYtPbt26e//OUv39vvV7/6la666qp+qAhnGj16tNUlYJCxBXj/Ul6vVw6HQx6PR3a73epyAJyFzs5O3XDDDd/br6KiQtHR0f1QEYC+0tPrN3d2DNLY2CiPx2N1GYDl7rrrLq1Zs+Y72z/55JN+rAiIPA6HQ8nJyVaX0S+MCTtlZWV68skn5Xa7NWHCBD377LOD6hZ1Y2Oj8m5bqI72NqtLASLedwUhYLCIGRKrDetfGhSBx4hXz//+979rxYoVKikp0bvvvqsJEyYoNzdXTU1NVpfWbzweD0EHANBjHe1tg+ZpgBFh509/+pPuvPNOLV68WBkZGVqzZo2GDRumv/71r1aX1m8cDodihsRaXQYAYICIGRIrh8NhdRn9YsA/xmpra9OBAwdUVFQU3BYVFaWcnBy5XK5uf+Pz+eTz+YLrXq+3z+vsa8nJydqw/qVBk9Ij2Weffabf//73VpcBRKzf/va3GjNmjNVlDHqM2RlAvvjiC3V2dn7jH1hycrIOHTrU7W9KS0u1cuXK/iivXyUnJw+a/+NGMl49B74bkwqivw34sPNDFBUVacWKFcF1r9ertLQ0CyuCSc455xwmTAOACDLgw855552n6OhoNTY2hmxvbGyU0+ns9jdxcXGKi4vrj/IAAIDFBvwA5djYWE2ePFkVFRXBbX6/XxUVFcrOzrawMgAAEAkG/J0dSVqxYoXy8/N1xRVX6KqrrtIzzzyj1tZWLV682OrSAACAxYwIO7feequOHz+uhx56SG63WxMnTtS2bdsYrAsAAPg2lsS3sQAAGIh6ev0e8GN2AAAAvgthBwAAGI2wAwAAjEbYAQAARiPsAAAAoxF2AACA0Qg7AADAaIQdAABgNCNmUD5bXfMqer1eiysBAAA91XXd/r75kQk7kk6cOCFJSktLs7gSAADQWydOnJDD4fjWdj4Xoa++kl5fX68RI0bIZrNZXQ6AMPJ6vUpLS9OxY8f4HAxgmEAgoBMnTig1NVVRUd8+MoewA8BofPsOAAOUAQCA0Qg7AADAaIQdAEaLi4tTSUmJ4uLirC4FgEUYswMAAIzGnR0AAGA0wg4AADAaYQcAABiNsAMAAIxG2AEAAEYj7AAAAKMRdgAAgNEIOwAAwGj/D31qvr/zUNgzAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Recalculating FSH/LH\n",
    "pcos_df['FSH/LH']= pcos_df['FSH(mIU/mL)']/pcos_df['LH(mIU/mL)']\n",
    "\n",
    "print(pcos_df['FSH/LH'].describe())\n",
    "\n",
    "\n",
    "sns.boxplot(pcos_df['FSH/LH'])\n",
    "#decide what to do with 327"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "jFFUx9-Mbf9H",
    "outputId": "471ef7c2-caf0-4c9f-d7f3-897a9897166b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "FSH(mIU/mL)     65.4\n",
       "LH(mIU/mL)       0.2\n",
       "FSH/LH         327.0\n",
       "Name: 250, dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "pcos_df.loc[250,['FSH(mIU/mL)','LH(mIU/mL)','FSH/LH']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "id": "uoAAr3T_fT-n"
   },
   "outputs": [],
   "source": [
    "pcos_df.drop(pcos_df.loc[pcos_df['FSH/LH']==327].index, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "3ereXL4fRd0B",
    "outputId": "a1cdfcb9-1865-42a8-c55f-bfb4cf3419e7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    540.000000\n",
      "mean       5.161187\n",
      "std        3.665392\n",
      "min        0.210000\n",
      "25%        3.300000\n",
      "50%        4.845000\n",
      "75%        6.400000\n",
      "max       60.370000\n",
      "Name: FSH(mIU/mL), dtype: float64\n",
      "count    540.000000\n",
      "mean       2.748752\n",
      "std        2.305150\n",
      "min        0.020000\n",
      "25%        1.020000\n",
      "50%        2.300000\n",
      "75%        3.672500\n",
      "max       14.690000\n",
      "Name: LH(mIU/mL), dtype: float64\n",
      "count    540.000000\n",
      "mean       3.775783\n",
      "std        5.381258\n",
      "min        0.228261\n",
      "25%        1.415529\n",
      "50%        2.160749\n",
      "75%        3.888314\n",
      "max       61.875000\n",
      "Name: FSH/LH, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(pcos_df['FSH(mIU/mL)'].describe())\n",
    "print(pcos_df['LH(mIU/mL)'].describe())\n",
    "print(pcos_df['FSH/LH'].describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 507
    },
    "id": "cxytbIHvUlTk",
    "outputId": "d014643b-b9ef-4899-feae-864e7d985165"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    540.000000\n",
      "mean       2.981320\n",
      "std        3.759014\n",
      "min        0.040000\n",
      "25%        1.480000\n",
      "50%        2.250000\n",
      "75%        3.570000\n",
      "max       65.000000\n",
      "Name: TSH (mIU/L), dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='TSH (mIU/L)'>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "print(pcos_df['TSH (mIU/L)'].describe())\n",
    "sns.boxplot(pcos_df['TSH (mIU/L)'])\n",
    "#This data has many outliers but sometimes you can have TSH very high. need to reserach more"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 507
    },
    "id": "zYhjt33EUnkx",
    "outputId": "2e53d966-86ae-4092-dfd7-7432c626396f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    540.000000\n",
      "mean       5.610487\n",
      "std        5.877446\n",
      "min        0.100000\n",
      "25%        2.010000\n",
      "50%        3.700000\n",
      "75%        6.870000\n",
      "max       66.000000\n",
      "Name: AMH(ng/mL), dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='AMH(ng/mL)'>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pcos_df[\"AMH(ng/mL)\"] = pd.to_numeric(pcos_df[\"AMH(ng/mL)\"], errors='coerce')\n",
    "print(pcos_df['AMH(ng/mL)'].describe())\n",
    "sns.boxplot(pcos_df['AMH(ng/mL)'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "vXgaLjUzlDCJ",
    "outputId": "ebbcc8e7-a1e4-480c-89ed-0fa2982e3400"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    540.000000\n",
      "mean       4.272950\n",
      "std        2.654525\n",
      "min        0.100000\n",
      "25%        2.010000\n",
      "50%        3.700000\n",
      "75%        6.870000\n",
      "max        8.160000\n",
      "Name: AMH(ng/mL), dtype: float64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\User\\AppData\\Local\\Temp\\ipykernel_19708\\3375953379.py:1: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
      "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
      "\n",
      "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
      "\n",
      "\n",
      "  pcos_df['AMH(ng/mL)'].mask(pcos_df['AMH(ng/mL)'] >8.16 ,8.16, inplace=True)\n"
     ]
    }
   ],
   "source": [
    "pcos_df['AMH(ng/mL)'].mask(pcos_df['AMH(ng/mL)'] >8.16 ,8.16, inplace=True)\n",
    "print(pcos_df['AMH(ng/mL)'].describe())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 507
    },
    "id": "w7oZ4sQEW0Cu",
    "outputId": "f99df15e-9f4b-4be5-ca4a-4ec1355318e2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    540.000000\n",
      "mean      24.326352\n",
      "std       14.983845\n",
      "min        0.400000\n",
      "25%       14.497500\n",
      "50%       21.920000\n",
      "75%       29.910000\n",
      "max      128.240000\n",
      "Name: PRL(ng/mL), dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='PRL(ng/mL)'>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#PRL(ng/mL):Prolactin blood test\n",
    "print(pcos_df['PRL(ng/mL)'].describe())\n",
    "sns.boxplot(pcos_df['PRL(ng/mL)'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "OxlonXOPLsSS"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 507
    },
    "id": "gw4g1e8LauoC",
    "outputId": "f41b25ea-1a94-4a21-d499-f0c9ba62caff"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count     540.000000\n",
      "mean       49.973681\n",
      "std       346.524993\n",
      "min         0.000000\n",
      "25%        20.800000\n",
      "50%        25.950000\n",
      "75%        34.550000\n",
      "max      6014.660000\n",
      "Name: Vit D3 (ng/mL), dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='Vit D3 (ng/mL)'>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(pcos_df['Vit D3 (ng/mL)'].describe())\n",
    "sns.boxplot(pcos_df['Vit D3 (ng/mL)'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 507
    },
    "id": "jaUBw3zma6h2",
    "outputId": "302a721e-8c86-4724-d9f7-1d94400d1d5c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    540.000000\n",
      "mean      28.986065\n",
      "std       12.587204\n",
      "min        0.000000\n",
      "25%       20.800000\n",
      "50%       25.950000\n",
      "75%       34.550000\n",
      "max       90.000000\n",
      "Name: Vit D3 (ng/mL), dtype: float64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\User\\AppData\\Local\\Temp\\ipykernel_19708\\3627547533.py:4: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
      "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
      "\n",
      "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
      "\n",
      "\n",
      "  pcos_df['Vit D3 (ng/mL)'].mask(pcos_df['Vit D3 (ng/mL)'] > 1000 ,pcos_df['Vit D3 (ng/mL)'].mean() , inplace=True)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='Vit D3 (ng/mL)'>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#cleaning vit d\n",
    "#Blood levels in the range of 20–30 ng/mL are usually considered sufficient. The safe upper limit is considered to be about 60 ng/mL, but people with symptoms of toxicity usually have levels above 150 ng/mL.\n",
    "\n",
    "pcos_df['Vit D3 (ng/mL)'].mask(pcos_df['Vit D3 (ng/mL)'] > 1000 ,pcos_df['Vit D3 (ng/mL)'].mean() , inplace=True)\n",
    "print(pcos_df['Vit D3 (ng/mL)'].describe())\n",
    "sns.boxplot(pcos_df['Vit D3 (ng/mL)'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "st-HSZ37cm97",
    "outputId": "7eba024a-0861-4087-d0a3-74a516398619"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Unnamed: 0', 'PCOS (Y/N)', ' Age (yrs)', 'Weight (Kg)', 'Height(Cm) ',\n",
       "       'BMI', 'Blood Group', 'Pulse rate(bpm) ', 'RR (breaths/min)',\n",
       "       'Hb(g/dl)', 'Cycle(R/I)', 'Cycle length(days)', 'Marraige Status (Yrs)',\n",
       "       'Pregnant(Y/N)', 'No. of abortions', '  I   beta-HCG(mIU/mL)',\n",
       "       'II    beta-HCG(mIU/mL)', 'FSH(mIU/mL)', 'LH(mIU/mL)', 'FSH/LH',\n",
       "       'Hip(inch)', 'Waist(inch)', 'Waist:Hip Ratio', 'TSH (mIU/L)',\n",
       "       'AMH(ng/mL)', 'PRL(ng/mL)', 'Vit D3 (ng/mL)', 'PRG(ng/mL)',\n",
       "       'RBS(mg/dl)', 'Weight gain(Y/N)', 'hair growth(Y/N)',\n",
       "       'Skin darkening (Y/N)', 'Hair loss(Y/N)', 'Pimples(Y/N)',\n",
       "       'Fast food (Y/N)', 'Reg.Exercise(Y/N)', 'BP _Systolic (mmHg)',\n",
       "       'BP _Diastolic (mmHg)', 'Follicle No. (L)', 'Follicle No. (R)',\n",
       "       'Avg. F size (L) (mm)', 'Avg. F size (R) (mm)', 'Endometrium (mm)'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pcos_df.columns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "T2e69L6BV2eq"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 507
    },
    "id": "dEzfVGS7LuG1",
    "outputId": "7b24ba8b-22d9-4775-b1c3-8b5edd52db2f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    540.000000\n",
      "mean       0.611613\n",
      "std        3.812353\n",
      "min        0.047000\n",
      "25%        0.250000\n",
      "50%        0.320000\n",
      "75%        0.452500\n",
      "max       85.000000\n",
      "Name: PRG(ng/mL), dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='PRG(ng/mL)'>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#PRG(ng/mL):serum progesterone test \n",
    "#men, postmenopausal women, and women at the beginning of their menstrual cycle: 1 ng/mL or under || pregnant women in their second trimester: 25.6 to 89.4 ng/mL\n",
    "print(pcos_df['PRG(ng/mL)'].describe())\n",
    "sns.boxplot(pcos_df['PRG(ng/mL)'])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 507
    },
    "id": "0O8CDmMoTS9H",
    "outputId": "72fe23e0-d4bc-4ca9-9af8-9fdd1e40aa3e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    540.000000\n",
      "mean      99.861481\n",
      "std       18.566926\n",
      "min       60.000000\n",
      "25%       92.000000\n",
      "50%      100.000000\n",
      "75%      107.000000\n",
      "max      350.000000\n",
      "Name: RBS(mg/dl), dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='RBS(mg/dl)'>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#RBS: Random Blood Sugar (RBS) Test\n",
    "#If blood sugar higher than usually 200 (mg/dL) to 350 mg/dL in adults and 200 mg/dL to 240 mg/dL in children, then mild symptoms of high blood sugar.\n",
    "\n",
    "print(pcos_df['RBS(mg/dl)'].describe())\n",
    "sns.boxplot(pcos_df['RBS(mg/dl)'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 508
    },
    "id": "Bxp6A81TV7u9",
    "outputId": "51149d1d-78e2-4e4d-e78f-a842f3cd5691"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    540.000000\n",
      "mean     114.833333\n",
      "std        5.919215\n",
      "min      100.000000\n",
      "25%      110.000000\n",
      "50%      110.000000\n",
      "75%      120.000000\n",
      "max      140.000000\n",
      "Name: BP _Systolic (mmHg), dtype: float64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\User\\AppData\\Local\\Temp\\ipykernel_19708\\337086684.py:2: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
      "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
      "\n",
      "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
      "\n",
      "\n",
      "  pcos_df['BP _Systolic (mmHg)'].mask(pcos_df['BP _Systolic (mmHg)'] < 80 ,pcos_df['BP _Systolic (mmHg)'].median() , inplace=True)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='BP _Systolic (mmHg)'>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#BP _Systolic (mmHg)\n",
    "pcos_df['BP _Systolic (mmHg)'].mask(pcos_df['BP _Systolic (mmHg)'] < 80 ,pcos_df['BP _Systolic (mmHg)'].median() , inplace=True)\n",
    "print(pcos_df['BP _Systolic (mmHg)'].describe())\n",
    "sns.boxplot(pcos_df['BP _Systolic (mmHg)'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 508
    },
    "id": "UWKJfq8dXIid",
    "outputId": "04b93798-4e99-4b69-b0be-6a21eb7314f7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    540.000000\n",
      "mean      77.055556\n",
      "std        4.722019\n",
      "min       60.000000\n",
      "25%       70.000000\n",
      "50%       80.000000\n",
      "75%       80.000000\n",
      "max      100.000000\n",
      "Name: BP _Diastolic (mmHg), dtype: float64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\User\\AppData\\Local\\Temp\\ipykernel_19708\\1253055114.py:2: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
      "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
      "\n",
      "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
      "\n",
      "\n",
      "  pcos_df['BP _Diastolic (mmHg)'].mask(pcos_df['BP _Diastolic (mmHg)'] < 50 ,pcos_df['BP _Diastolic (mmHg)'].median() , inplace=True)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='BP _Diastolic (mmHg)'>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#BP _Diastolic (mmHg)\n",
    "pcos_df['BP _Diastolic (mmHg)'].mask(pcos_df['BP _Diastolic (mmHg)'] < 50 ,pcos_df['BP _Diastolic (mmHg)'].median() , inplace=True)\n",
    "\n",
    "print(pcos_df['BP _Diastolic (mmHg)'].describe())\n",
    "sns.boxplot(pcos_df['BP _Diastolic (mmHg)'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 698
    },
    "id": "cfHwmM6YX5lc",
    "outputId": "323b8f15-902e-420b-a432-a4a8472f4ea4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    540.000000\n",
      "mean       6.122222\n",
      "std        4.229925\n",
      "min        0.000000\n",
      "25%        3.000000\n",
      "50%        5.000000\n",
      "75%        9.000000\n",
      "max       22.000000\n",
      "Name: Follicle No. (L), dtype: float64\n",
      "count    540.000000\n",
      "mean       6.625926\n",
      "std        4.426358\n",
      "min        0.000000\n",
      "25%        3.000000\n",
      "50%        6.000000\n",
      "75%       10.000000\n",
      "max       20.000000\n",
      "Name: Follicle No. (R), dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='Follicle No. (L)'>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "#'Follicle No. (L)', \n",
    "print(pcos_df['Follicle No. (L)'].describe())\n",
    "sns.boxplot(pcos_df['Follicle No. (L)'])\n",
    "\n",
    "#'Follicle No. (R)'\n",
    "print(pcos_df['Follicle No. (R)'].describe())\n",
    "sns.boxplot(pcos_df['Follicle No. (R)'])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 557
    },
    "id": "yiKyEwaoZsO9",
    "outputId": "f1b07f95-c9ea-4849-deb2-f5d680a2a8bc"
   },
   "outputs": [
    {
     "data": {
      "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>Unnamed: 0</th>\n",
       "      <th>PCOS (Y/N)</th>\n",
       "      <th>Age (yrs)</th>\n",
       "      <th>Weight (Kg)</th>\n",
       "      <th>Height(Cm)</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Blood Group</th>\n",
       "      <th>Pulse rate(bpm)</th>\n",
       "      <th>RR (breaths/min)</th>\n",
       "      <th>Hb(g/dl)</th>\n",
       "      <th>...</th>\n",
       "      <th>Pimples(Y/N)</th>\n",
       "      <th>Fast food (Y/N)</th>\n",
       "      <th>Reg.Exercise(Y/N)</th>\n",
       "      <th>BP _Systolic (mmHg)</th>\n",
       "      <th>BP _Diastolic (mmHg)</th>\n",
       "      <th>Follicle No. (L)</th>\n",
       "      <th>Follicle No. (R)</th>\n",
       "      <th>Avg. F size (L) (mm)</th>\n",
       "      <th>Avg. F size (R) (mm)</th>\n",
       "      <th>Endometrium (mm)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>103</td>\n",
       "      <td>0</td>\n",
       "      <td>34</td>\n",
       "      <td>57.0</td>\n",
       "      <td>160.000</td>\n",
       "      <td>22.3</td>\n",
       "      <td>15</td>\n",
       "      <td>72</td>\n",
       "      <td>20</td>\n",
       "      <td>11.8</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>127</td>\n",
       "      <td>0</td>\n",
       "      <td>42</td>\n",
       "      <td>48.0</td>\n",
       "      <td>148.000</td>\n",
       "      <td>21.9</td>\n",
       "      <td>11</td>\n",
       "      <td>72</td>\n",
       "      <td>18</td>\n",
       "      <td>11.2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>110</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261</th>\n",
       "      <td>261</td>\n",
       "      <td>0</td>\n",
       "      <td>32</td>\n",
       "      <td>35.0</td>\n",
       "      <td>150.000</td>\n",
       "      <td>15.6</td>\n",
       "      <td>11</td>\n",
       "      <td>70</td>\n",
       "      <td>18</td>\n",
       "      <td>11.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>110</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>288</th>\n",
       "      <td>288</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>66.0</td>\n",
       "      <td>155.000</td>\n",
       "      <td>27.5</td>\n",
       "      <td>15</td>\n",
       "      <td>78</td>\n",
       "      <td>22</td>\n",
       "      <td>10.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>110</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>7.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>298</td>\n",
       "      <td>0</td>\n",
       "      <td>34</td>\n",
       "      <td>67.0</td>\n",
       "      <td>152.000</td>\n",
       "      <td>29.0</td>\n",
       "      <td>15</td>\n",
       "      <td>74</td>\n",
       "      <td>20</td>\n",
       "      <td>11.5</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>7.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>299</td>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
       "      <td>50.0</td>\n",
       "      <td>152.000</td>\n",
       "      <td>21.6</td>\n",
       "      <td>11</td>\n",
       "      <td>72</td>\n",
       "      <td>20</td>\n",
       "      <td>11.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>120</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>409</th>\n",
       "      <td>409</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>60.0</td>\n",
       "      <td>161.000</td>\n",
       "      <td>23.1</td>\n",
       "      <td>13</td>\n",
       "      <td>74</td>\n",
       "      <td>20</td>\n",
       "      <td>11.2</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>110</td>\n",
       "      <td>70</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>528</th>\n",
       "      <td>528</td>\n",
       "      <td>0</td>\n",
       "      <td>41</td>\n",
       "      <td>58.0</td>\n",
       "      <td>146.304</td>\n",
       "      <td>27.1</td>\n",
       "      <td>13</td>\n",
       "      <td>72</td>\n",
       "      <td>20</td>\n",
       "      <td>10.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>9.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>531</th>\n",
       "      <td>531</td>\n",
       "      <td>0</td>\n",
       "      <td>34</td>\n",
       "      <td>70.0</td>\n",
       "      <td>164.000</td>\n",
       "      <td>26.0</td>\n",
       "      <td>11</td>\n",
       "      <td>72</td>\n",
       "      <td>22</td>\n",
       "      <td>10.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>70</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>8.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>536</th>\n",
       "      <td>536</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>50.0</td>\n",
       "      <td>164.592</td>\n",
       "      <td>18.5</td>\n",
       "      <td>17</td>\n",
       "      <td>72</td>\n",
       "      <td>16</td>\n",
       "      <td>11.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>110</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>17.5</td>\n",
       "      <td>10.0</td>\n",
       "      <td>6.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>538</th>\n",
       "      <td>538</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>54.0</td>\n",
       "      <td>152.000</td>\n",
       "      <td>23.4</td>\n",
       "      <td>13</td>\n",
       "      <td>74</td>\n",
       "      <td>20</td>\n",
       "      <td>10.8</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>110</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>7.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11 rows × 43 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Unnamed: 0  PCOS (Y/N)   Age (yrs)  Weight (Kg)  Height(Cm)    BMI  \\\n",
       "103         103           0          34         57.0      160.000  22.3   \n",
       "127         127           0          42         48.0      148.000  21.9   \n",
       "261         261           0          32         35.0      150.000  15.6   \n",
       "288         288           0          35         66.0      155.000  27.5   \n",
       "298         298           0          34         67.0      152.000  29.0   \n",
       "299         299           0          26         50.0      152.000  21.6   \n",
       "409         409           0          40         60.0      161.000  23.1   \n",
       "528         528           0          41         58.0      146.304  27.1   \n",
       "531         531           0          34         70.0      164.000  26.0   \n",
       "536         536           0          35         50.0      164.592  18.5   \n",
       "538         538           0          36         54.0      152.000  23.4   \n",
       "\n",
       "     Blood Group  Pulse rate(bpm)   RR (breaths/min)  Hb(g/dl)  ...  \\\n",
       "103           15                72                20      11.8  ...   \n",
       "127           11                72                18      11.2  ...   \n",
       "261           11                70                18      11.5  ...   \n",
       "288           15                78                22      10.5  ...   \n",
       "298           15                74                20      11.5  ...   \n",
       "299           11                72                20      11.0  ...   \n",
       "409           13                74                20      11.2  ...   \n",
       "528           13                72                20      10.5  ...   \n",
       "531           11                72                22      10.5  ...   \n",
       "536           17                72                16      11.0  ...   \n",
       "538           13                74                20      10.8  ...   \n",
       "\n",
       "     Pimples(Y/N)  Fast food (Y/N)  Reg.Exercise(Y/N)  BP _Systolic (mmHg)  \\\n",
       "103             0              0.0                  0                  120   \n",
       "127             0              0.0                  0                  110   \n",
       "261             0              1.0                  0                  110   \n",
       "288             0              1.0                  0                  110   \n",
       "298             1              1.0                  0                  120   \n",
       "299             0              1.0                  1                  120   \n",
       "409             1              0.0                  0                  110   \n",
       "528             0              1.0                  0                  120   \n",
       "531             0              0.0                  0                  120   \n",
       "536             0              0.0                  0                  110   \n",
       "538             0              0.0                  0                  110   \n",
       "\n",
       "     BP _Diastolic (mmHg)  Follicle No. (L)  Follicle No. (R)  \\\n",
       "103                    80                 1                 0   \n",
       "127                    70                 1                 0   \n",
       "261                    80                 1                 0   \n",
       "288                    80                 1                 0   \n",
       "298                    70                 1                 0   \n",
       "299                    80                 1                 0   \n",
       "409                    70                 3                 0   \n",
       "528                    80                 1                 0   \n",
       "531                    70                 2                 0   \n",
       "536                    70                 1                 0   \n",
       "538                    80                 1                 0   \n",
       "\n",
       "     Avg. F size (L) (mm)  Avg. F size (R) (mm)  Endometrium (mm)  \n",
       "103                  14.0                   0.0               7.0  \n",
       "127                  17.0                  15.0               9.0  \n",
       "261                  19.0                  20.0              10.0  \n",
       "288                  15.0                  18.0               7.7  \n",
       "298                  16.0                  12.0               7.7  \n",
       "299                  15.0                  18.0              11.0  \n",
       "409                  10.0                  11.0               4.0  \n",
       "528                  19.0                  14.0               9.7  \n",
       "531                  21.0                  16.0               8.5  \n",
       "536                  17.5                  10.0               6.7  \n",
       "538                  18.0                   9.0               7.3  \n",
       "\n",
       "[11 rows x 43 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pcos_df.loc[pcos_df['Follicle No. (R)'] == 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 698
    },
    "id": "Ax6R71PFY3X8",
    "outputId": "3cc1470e-b66d-419b-d434-31715f64825a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    540.000000\n",
      "mean      15.018148\n",
      "std        3.570146\n",
      "min        0.000000\n",
      "25%       13.000000\n",
      "50%       15.000000\n",
      "75%       18.000000\n",
      "max       24.000000\n",
      "Name: Avg. F size (L) (mm), dtype: float64\n",
      "count    540.000000\n",
      "mean      15.448833\n",
      "std        3.321255\n",
      "min        0.000000\n",
      "25%       13.000000\n",
      "50%       16.000000\n",
      "75%       18.000000\n",
      "max       24.000000\n",
      "Name: Avg. F size (R) (mm), dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='Avg. F size (L) (mm)'>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#'Avg. F size (L) (mm)'\n",
    "\n",
    "print(pcos_df['Avg. F size (L) (mm)'].describe())\n",
    "sns.boxplot(pcos_df['Avg. F size (L) (mm)'])\n",
    "#'Avg. F size (R) (mm)'\n",
    "print(pcos_df['Avg. F size (R) (mm)'].describe())\n",
    "sns.boxplot(pcos_df['Avg. F size (R) (mm)'])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 507
    },
    "id": "nCKb3oTicO3c",
    "outputId": "d7f73ad7-2333-420f-cf9e-367f551a1f05"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    540.000000\n",
      "mean       8.483648\n",
      "std        2.141611\n",
      "min        2.000000\n",
      "25%        7.000000\n",
      "50%        8.500000\n",
      "75%        9.800000\n",
      "max       18.000000\n",
      "Name: Endometrium (mm), dtype: float64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\User\\AppData\\Local\\Temp\\ipykernel_19708\\817603977.py:2: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
      "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
      "\n",
      "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
      "\n",
      "\n",
      "  pcos_df['Endometrium (mm)'].mask(pcos_df['Endometrium (mm)'] < 2 ,2 , inplace=True)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='Endometrium (mm)'>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Endometrium (mm) :The layer of tissue that lines the uterus.\n",
    "pcos_df['Endometrium (mm)'].mask(pcos_df['Endometrium (mm)'] < 2 ,2 , inplace=True)\n",
    "print(pcos_df['Endometrium (mm)'].describe())\n",
    "sns.boxplot(pcos_df['Endometrium (mm)'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "id": "Vu90dHggHokC"
   },
   "outputs": [],
   "source": [
    "#Function to determine outliers\n",
    "def outlier_thresholds(dataframe, variable):\n",
    "    quartile1 = dataframe[variable].quantile(0.05)\n",
    "    quartile3 = dataframe[variable].quantile(0.95)\n",
    "    interquantile_range = quartile3 - quartile1\n",
    "    up_limit = quartile3 + 1.5 * interquantile_range\n",
    "    low_limit = quartile1 - 1.5 * interquantile_range\n",
    "    return low_limit, up_limit\n",
    "\n",
    "def outliers(dataframe, num_col_names, plot=False):\n",
    "    variable_names = []\n",
    "    for col in num_col_names:\n",
    "        low_limit, up_limit = outlier_thresholds(dataframe, col)\n",
    "        if dataframe[(dataframe[col] > up_limit) | (dataframe[col] < low_limit)].any(axis=None):\n",
    "            number_of_outliers = dataframe[(dataframe[col] > up_limit) | (dataframe[col] < low_limit)].shape[0]\n",
    "            print(col, \":\", number_of_outliers)\n",
    "            \n",
    "            \n",
    "    return variable_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "DZwRcO9SHrXD",
    "outputId": "323316b3-5bf0-4710-9c3b-34fa061b9202"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  I   beta-HCG(mIU/mL) : 14\n",
      "II    beta-HCG(mIU/mL) : 7\n",
      "FSH(mIU/mL) : 3\n",
      "FSH/LH : 8\n",
      "TSH (mIU/L) : 9\n",
      "PRL(ng/mL) : 2\n",
      "PRG(ng/mL) : 5\n",
      "RBS(mg/dl) : 2\n",
      "BP _Systolic (mmHg) : 2\n",
      "BP _Diastolic (mmHg) : 1\n"
     ]
    }
   ],
   "source": [
    "outliers(pcos_df,pcos_df.columns).sort(reverse=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "id": "aebHafFIJcef"
   },
   "outputs": [],
   "source": [
    "def find_outliers_IQR(df):\n",
    "\n",
    "   q1=df.quantile(0.25)\n",
    "\n",
    "   q3=df.quantile(0.75)\n",
    "\n",
    "   IQR=q3-q1\n",
    "\n",
    "   outliers_detect = pcos_df[((pcos_df<(q1-1.5*IQR)) | (pcos_df>(q3+1.5*IQR)))]\n",
    "\n",
    "   return outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "T3K8dY2OLXRS"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 424
    },
    "id": "w1vqtaTpL5eH",
    "outputId": "2d77d30c-e0c1-4eba-d382-dfb0c62fa265"
   },
   "outputs": [
    {
     "data": {
      "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",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FSH(mIU/mL)</th>\n",
       "      <th>LH(mIU/mL)</th>\n",
       "      <th>FSH/LH</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.95</td>\n",
       "      <td>3.68</td>\n",
       "      <td>2.160326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6.73</td>\n",
       "      <td>1.09</td>\n",
       "      <td>6.174312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.54</td>\n",
       "      <td>0.88</td>\n",
       "      <td>6.295455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8.06</td>\n",
       "      <td>2.36</td>\n",
       "      <td>3.415254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.98</td>\n",
       "      <td>0.90</td>\n",
       "      <td>4.422222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>536</th>\n",
       "      <td>10.06</td>\n",
       "      <td>1.81</td>\n",
       "      <td>5.558011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>537</th>\n",
       "      <td>5.07</td>\n",
       "      <td>2.84</td>\n",
       "      <td>1.785211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>538</th>\n",
       "      <td>11.96</td>\n",
       "      <td>2.78</td>\n",
       "      <td>4.302158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>539</th>\n",
       "      <td>4.40</td>\n",
       "      <td>4.33</td>\n",
       "      <td>1.016166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>540</th>\n",
       "      <td>3.99</td>\n",
       "      <td>4.30</td>\n",
       "      <td>0.927907</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>540 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     FSH(mIU/mL)  LH(mIU/mL)    FSH/LH\n",
       "0           7.95        3.68  2.160326\n",
       "1           6.73        1.09  6.174312\n",
       "2           5.54        0.88  6.295455\n",
       "3           8.06        2.36  3.415254\n",
       "4           3.98        0.90  4.422222\n",
       "..           ...         ...       ...\n",
       "536        10.06        1.81  5.558011\n",
       "537         5.07        2.84  1.785211\n",
       "538        11.96        2.78  4.302158\n",
       "539         4.40        4.33  1.016166\n",
       "540         3.99        4.30  0.927907\n",
       "\n",
       "[540 rows x 3 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pcos_df[['FSH(mIU/mL)','LH(mIU/mL)','FSH/LH']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "id": "jzY_AVOzLYkO"
   },
   "outputs": [],
   "source": [
    "pcos_df.to_csv('pcos_cleaned.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "D8AYtnc0NuYg"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 428
    },
    "id": "bJ-FwesxLk3p",
    "outputId": "cd87e7e3-cbcb-41b8-d39b-72b37969f01f"
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>PCOS (Y/N)</th>\n",
       "      <th>Age (yrs)</th>\n",
       "      <th>Weight (Kg)</th>\n",
       "      <th>Height(Cm)</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Blood Group</th>\n",
       "      <th>Pulse rate(bpm)</th>\n",
       "      <th>RR (breaths/min)</th>\n",
       "      <th>Hb(g/dl)</th>\n",
       "      <th>...</th>\n",
       "      <th>Pimples(Y/N)</th>\n",
       "      <th>Fast food (Y/N)</th>\n",
       "      <th>Reg.Exercise(Y/N)</th>\n",
       "      <th>BP _Systolic (mmHg)</th>\n",
       "      <th>BP _Diastolic (mmHg)</th>\n",
       "      <th>Follicle No. (L)</th>\n",
       "      <th>Follicle No. (R)</th>\n",
       "      <th>Avg. F size (L) (mm)</th>\n",
       "      <th>Avg. F size (R) (mm)</th>\n",
       "      <th>Endometrium (mm)</th>\n",
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       "  </thead>\n",
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       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>540.000000</td>\n",
       "      <td>540.000000</td>\n",
       "      <td>540.000000</td>\n",
       "      <td>540.000000</td>\n",
       "      <td>540.000000</td>\n",
       "      <td>540.000000</td>\n",
       "      <td>540.000000</td>\n",
       "      <td>540.000000</td>\n",
       "      <td>540.000000</td>\n",
       "      <td>540.000000</td>\n",
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       "      <td>540.000000</td>\n",
       "      <td>540.000000</td>\n",
       "      <td>540.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>270.037037</td>\n",
       "      <td>0.325926</td>\n",
       "      <td>31.405556</td>\n",
       "      <td>59.590185</td>\n",
       "      <td>156.491289</td>\n",
       "      <td>24.285370</td>\n",
       "      <td>13.800000</td>\n",
       "      <td>73.459259</td>\n",
       "      <td>19.246296</td>\n",
       "      <td>11.158481</td>\n",
       "      <td>...</td>\n",
       "      <td>0.488889</td>\n",
       "      <td>0.514815</td>\n",
       "      <td>0.248148</td>\n",
       "      <td>114.833333</td>\n",
       "      <td>77.055556</td>\n",
       "      <td>6.122222</td>\n",
       "      <td>6.625926</td>\n",
       "      <td>15.018148</td>\n",
       "      <td>15.448833</td>\n",
       "      <td>8.483648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>156.460083</td>\n",
       "      <td>0.469154</td>\n",
       "      <td>5.384335</td>\n",
       "      <td>10.984220</td>\n",
       "      <td>6.037270</td>\n",
       "      <td>4.025824</td>\n",
       "      <td>1.841795</td>\n",
       "      <td>2.687877</td>\n",
       "      <td>1.689344</td>\n",
       "      <td>0.866952</td>\n",
       "      <td>...</td>\n",
       "      <td>0.500340</td>\n",
       "      <td>0.500244</td>\n",
       "      <td>0.432339</td>\n",
       "      <td>5.919215</td>\n",
       "      <td>4.722019</td>\n",
       "      <td>4.229925</td>\n",
       "      <td>4.426358</td>\n",
       "      <td>3.570146</td>\n",
       "      <td>3.321255</td>\n",
       "      <td>2.141611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>137.000000</td>\n",
       "      <td>12.400000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>8.500000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>134.750000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>27.750000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>152.000000</td>\n",
       "      <td>21.600000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>10.500000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>110.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>7.000000</td>\n",
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       "      <th>50%</th>\n",
       "      <td>270.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>156.000000</td>\n",
       "      <td>24.200000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>110.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>8.500000</td>\n",
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       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>405.250000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>160.000000</td>\n",
       "      <td>26.600000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>74.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>11.700000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>9.800000</td>\n",
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       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>540.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>108.000000</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>38.900000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>82.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>14.800000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>18.000000</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 43 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Unnamed: 0  PCOS (Y/N)   Age (yrs)  Weight (Kg)  Height(Cm)   \\\n",
       "count  540.000000  540.000000  540.000000   540.000000   540.000000   \n",
       "mean   270.037037    0.325926   31.405556    59.590185   156.491289   \n",
       "std    156.460083    0.469154    5.384335    10.984220     6.037270   \n",
       "min      0.000000    0.000000   20.000000    31.000000   137.000000   \n",
       "25%    134.750000    0.000000   27.750000    52.000000   152.000000   \n",
       "50%    270.500000    0.000000   31.000000    59.000000   156.000000   \n",
       "75%    405.250000    1.000000   35.000000    65.000000   160.000000   \n",
       "max    540.000000    1.000000   48.000000   108.000000   180.000000   \n",
       "\n",
       "              BMI  Blood Group  Pulse rate(bpm)   RR (breaths/min)  \\\n",
       "count  540.000000   540.000000        540.000000        540.000000   \n",
       "mean    24.285370    13.800000         73.459259         19.246296   \n",
       "std      4.025824     1.841795          2.687877          1.689344   \n",
       "min     12.400000    11.000000         70.000000         16.000000   \n",
       "25%     21.600000    13.000000         72.000000         18.000000   \n",
       "50%     24.200000    14.000000         72.000000         18.000000   \n",
       "75%     26.600000    15.000000         74.000000         20.000000   \n",
       "max     38.900000    18.000000         82.000000         28.000000   \n",
       "\n",
       "         Hb(g/dl)  ...  Pimples(Y/N)  Fast food (Y/N)  Reg.Exercise(Y/N)  \\\n",
       "count  540.000000  ...    540.000000       540.000000         540.000000   \n",
       "mean    11.158481  ...      0.488889         0.514815           0.248148   \n",
       "std      0.866952  ...      0.500340         0.500244           0.432339   \n",
       "min      8.500000  ...      0.000000         0.000000           0.000000   \n",
       "25%     10.500000  ...      0.000000         0.000000           0.000000   \n",
       "50%     11.000000  ...      0.000000         1.000000           0.000000   \n",
       "75%     11.700000  ...      1.000000         1.000000           0.000000   \n",
       "max     14.800000  ...      1.000000         1.000000           1.000000   \n",
       "\n",
       "       BP _Systolic (mmHg)  BP _Diastolic (mmHg)  Follicle No. (L)  \\\n",
       "count           540.000000            540.000000        540.000000   \n",
       "mean            114.833333             77.055556          6.122222   \n",
       "std               5.919215              4.722019          4.229925   \n",
       "min             100.000000             60.000000          0.000000   \n",
       "25%             110.000000             70.000000          3.000000   \n",
       "50%             110.000000             80.000000          5.000000   \n",
       "75%             120.000000             80.000000          9.000000   \n",
       "max             140.000000            100.000000         22.000000   \n",
       "\n",
       "       Follicle No. (R)  Avg. F size (L) (mm)  Avg. F size (R) (mm)  \\\n",
       "count        540.000000            540.000000            540.000000   \n",
       "mean           6.625926             15.018148             15.448833   \n",
       "std            4.426358              3.570146              3.321255   \n",
       "min            0.000000              0.000000              0.000000   \n",
       "25%            3.000000             13.000000             13.000000   \n",
       "50%            6.000000             15.000000             16.000000   \n",
       "75%           10.000000             18.000000             18.000000   \n",
       "max           20.000000             24.000000             24.000000   \n",
       "\n",
       "       Endometrium (mm)  \n",
       "count        540.000000  \n",
       "mean           8.483648  \n",
       "std            2.141611  \n",
       "min            2.000000  \n",
       "25%            7.000000  \n",
       "50%            8.500000  \n",
       "75%            9.800000  \n",
       "max           18.000000  \n",
       "\n",
       "[8 rows x 43 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pcos_df.describe()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "outputs": [],
   "source": []
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