Update README.md

README.md

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----
-license: mit
----
+---
+license: mit
+---
+# 🍇 Grape Firmness AutoML Model
+
+## Model Details
+- **Model type**: RandomForest Regressor (selected by AutoML search)
+- **Framework**: scikit-learn
+- **Preprocessing**: Custom feature engineering and scaling (`preprocess.joblib`)
+- **Files**:
+  - `model.joblib` — trained RandomForest model
+  - `preprocess.joblib` — preprocessing pipeline (feature transforms)
+
+---
+
+## Task
+**Regression**: Predict grape firmness (continuous value) from structured features.  
+This task is designed to explore *Classical AutoML* approaches for tabular datasets.
+
+---
+
+## Dataset
+- **Source**: [rlogh/grape-firmness-dataset](https://huggingface.co/datasets/rlogh/grape-firmness-dataset)  
+- **Samples**: ~X rows, Y features (replace with actual numbers if needed)  
+- **Split**: 70% train, 15% validation, 15% test  
+- **Target**: `firmness` (numerical label)
+
+---
+
+## Training Procedure
+- **AutoML approach**: grid search across models (RandomForest, SVR, etc.)
+- **Search space**:
+  - `RandomForestRegressor` — tuned `n_estimators`, `max_depth`
+  - `SVR` — tuned `C`, `kernel`
+- **Evaluation metric**: R² score (maximize)
+- **Validation**: stratified split for regression
+
+---
+
+## Results
+On the **held-out test set**:
+- **RMSE**: 0.177  
+- **R²**: 0.943  
+
+The RandomForest Regressor achieved the best performance.
+
+---
+
+## Limitations
+- **Small dataset size** → may not generalize to unseen grape varieties or measurement settings.
+- **Not production-ready** → purely academic demonstration.
+- **Feature engineering is minimal** → no domain knowledge incorporated.
+
+---
+
+## How to Use
+
+### Install requirements
+```bash
+pip install scikit-learn joblib
+import joblib
+import numpy as np
+
+# Load preprocess and model
+preprocess = joblib.load("preprocess.joblib")
+model = joblib.load("model.joblib")
+
+# Example input: replace with real sample (shape must match training features)
+X_new = np.array([[5.1, 3.5, 1.4, 0.2]])  # dummy
+X_new_p = preprocess.transform(X_new)
+
+# Predict firmness
+y_pred = model.predict(X_new_p)
+print("Predicted firmness:", y_pred)
+
+## Dependencies
+
+numpy==1.26.4
+
+scikit-learn==1.4.2
+
+pandas==2.2.2
+
+joblib
+
+@dataset{rlogh_grape_firmness_2024,
+  title={Grape Firmness Dataset},
+  author={rlogh},
+  year={2024},
+  url={https://huggingface.co/datasets/rlogh/grape-firmness-dataset}
+}