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README.md

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+---
+license: mit
+tags:
+- sales-forecasting
+- random-forest
+- regression
+- superkart
+---
+
+# SuperKart Sales Forecasting Model
+
+This is a Random Forest Regressor model trained to predict product sales at SuperKart stores.
+
+## Model Details
+
+- **Model Type:** Random Forest Regressor
+- **Task:** Regression (Sales Forecasting)
+- **Training Data:** SuperKart historical sales data (8,763 records)
+- **Test R² Score:** 0.9319
+- **Test RMSE:** $278.68
+
+## Best Hyperparameters
+
+- n_estimators: 200
+- max_depth: None
+- min_samples_split: 5
+- min_samples_leaf: 2
+
+## Features
+
+The model uses the following features:
+- Product_Weight
+- Product_Sugar_Content
+- Product_Allocated_Area
+- Product_Type
+- Product_MRP
+- Store_Size
+- Store_Location_City_Type
+- Store_Type
+- Store_Age
+- Price_Category
+
+## Usage
+```python
+import joblib
+import pandas as pd
+
+# Load model
+model = joblib.load('best_model.pkl')
+
+# Load label encoders
+label_encoders = joblib.load('label_encoders.pkl')
+
+# Make predictions
+predictions = model.predict(X_test)
+```
+
+## Performance Comparison
+
+| Model | Test R² Score | Test RMSE |
+|-------|--------------|-----------|
+| Random Forest | 0.9319 | $278.68 |
+| XGBoost | 0.9314 | $279.69 |
+| Gradient Boosting | 0.9290 | $284.58 |
+
+## Training Details
+
+- Train-Test Split: 80-20
+- Cross-Validation: 3-fold
+- Evaluation Metrics: RMSE, MAE, R²

best_model.pkl

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label_encoders.pkl

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