Upload superkart_sales_forecast.pkl

superkart_sales_forecast.pkl

@@ -1,54 +1,3 @@
-# model_export.py
-
-import joblib
-import pandas as pd
-from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor  # or whatever models you trained
-
-# Replace with your actual evaluation function
-def evaluate_model(y_true, y_pred):
-    from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score
-    return {
-        "RMSE": mean_squared_error(y_true, y_pred, squared=False),
-        "MAE": mean_absolute_error(y_true, y_pred),
-        "R2": r2_score(y_true, y_pred)
-    }
-
-# Assume rf_best_model and gb_best_model are already trained
-# Also assume we have performance_df with RMSE values
-
-# Example: Simulate performance_df (you should use the real one)
-# import pandas as pd
-# performance_df = pd.DataFrame({
-#     "RMSE": {
-#         "Random Forest (Tuned)": 113.52,
-#         "Gradient Boosting (Tuned)": 119.40
-#     }
-# })
-
-# -----------------------------
-# Select Final Model Based on RMSE
-# -----------------------------
-best_model = rf_best_model if performance_df["RMSE"].idxmin() == "Random Forest (Tuned)" else gb_best_model
-
-# Predict and evaluate
-final_predictions = best_model.predict(X_test)
-final_metrics = evaluate_model(y_test, final_predictions)
-
-print("\nBest Model Selected:", performance_df['RMSE'].idxmin())
-print("Performance on Test Set:", final_metrics)
-
-# -----------------------------
-# Serialize the Best Model
-# -----------------------------
-model_filename = "superkart_sales_forecast.pkl"
-joblib.dump(best_model, model_filename)
-print(f"\nModel 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("\nLoaded Model Performance on Test Set:", loaded_metrics)
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