Create superkart_sales_forecast.pkl

superkart_sales_forecast.pkl

@@ -0,0 +1,30 @@
+import joblib
+
+# -----------------------------
+# Select Final Model Based on RMSE
+# -----------------------------
+# 'rf_best_model' and 'gb_best_model' are tuned models (Random Forest, Gradient Boosting)
+best_model = rf_best_model if performance_df["RMSE"].idxmin() == "Random Forest (Tuned)" else gb_best_model
+
+# Make predictions and evaluate
+final_predictions = best_model.predict(X_test)
+final_metrics = evaluate_model(y_test, final_predictions)
+
+print("\n Best Model Selected:", performance_df['RMSE'].idxmin())
+print(" Performance on Test Set:", final_metrics)
+
+# -----------------------------
+# Serialize the Best Model
+# -----------------------------
+model_filename = "superkart_sales_forecast.pkl"  # Hugging Face expects this name
+joblib.dump(best_model, model_filename)
+print(f"\n Model serialized and saved as: {model_filename}")
+
+# -----------------------------
+# Load and Validate the Model
+# -----------------------------
+loaded_model = joblib.load(model_filename)
+loaded_predictions = loaded_model.predict(X_test)
+loaded_metrics = evaluate_model(y_test, loaded_predictions)
+
+print("\n Loaded Model Performance on Test Set:", loaded_metrics)