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README.md

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+---
+license: apache-2.0
+library_name: scikit-learn
+tags:
+- tabular-regression
+- sales-forecast
+- gradient-boosting
+- cross-sectional
+datasets:
+- dev02chandan/sales-forecast-dataset
+metrics:
+- rmse
+- mae
+- mape
+- smape
+---
+
+# Sales Forecast Model (GBR)
+
+**Task:** Predict `Product_Store_Sales_Total` from product and store attributes.  
+**Data:** dev02chandan/sales-forecast-dataset (`raw/SuperKart.csv` with processed train/test under `processed/`).  
+**Model:** GradientBoostingRegressor selected via GroupKFold CV on `Store_Id`.
+
+## Test Metrics
+- CV RMSE: 1157.1346565946897
+- RMSE: 1600.05837632221
+- MAE: 1405.5687461646362
+- MAPE: 27.069205177956633
+- sMAPE: 32.25248697544593
+
+## Usage
+```python
+from huggingface_hub import hf_hub_download
+import joblib, pandas as pd
+
+pkl_path = hf_hub_download(repo_id="dev02chandan/sales-forecast-model", filename="model.pkl", repo_type="model")
+model = joblib.load(pkl_path)
+
+# X must contain the same columns used in training (one-hot is inside the pipeline)
+# Example:
+# X = pd.DataFrame([...])
+# y_pred = model.predict(X)