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README.md

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+
+---
+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.).