| tags: | |
| - sklearn | |
| - regression | |
| - sales-forecast | |
| - RandomForest | |
| library_name: sklearn | |
| metrics: | |
| - rmse | |
| - r2 | |
| model-index: | |
| - name: RandomForest | |
| results: | |
| - task: | |
| type: tabular-regression | |
| name: Sales Forecasting | |
| dataset: | |
| name: SuperKart Data | |
| type: tabular | |
| metrics: | |
| - type: rmse | |
| value: 280.8543593979435 | |
| - type: r2 | |
| value: 0.9308695977150697 | |
| # SuperKart Sales Prediction Model | |
| ## Model Description | |
| This is a **RandomForest** model trained to predict sales revenue (`Product_Store_Sales_Total`) for SuperKart stores. | |
| It utilizes a Scikit-Learn Pipeline that handles: | |
| 1. **Preprocessing**: OneHotEncoding for categorical variables and Scaling for numerical variables. | |
| 2. **Modeling**: The best performing regressor selected from Random Forest, Gradient Boosting, and XGBoost. | |
| ## Performance | |
| - **RMSE**: 280.8544 | |
| - **R2 Score**: 0.9309 | |
| - **MAE**: 114.7186 | |
| ## Usage | |
| This model expects a pandas DataFrame with the same columns as the training set (Product_Weight, Product_Sugar_Content, etc.). | |