Enhanced the code and their functionality and works fine

.gitignore

@@ -3,8 +3,12 @@ __pycache__/
 *.py[cod]
 *$py.class
 
+models/
+data/processed/
 # C extensions
 *.so
+venv/
+.venv/
 
 # Distribution / packaging
 .Python

main.py

@@ -0,0 +1,43 @@
+from src.load_data import load_data, save_data
+from src.preprocessing import prepare_features
+from src.train_and_evaluate import train_and_evaluate
+from src.visualization import menu_plots
+from src.inference import save_model_artifacts, predict_from_input, load_model_artifacts
+import os
+
+DATA_PATH = "data/raw/AI-Data.csv"
+
+
+def main():
+
+    df = load_data(DATA_PATH)
+    if df is None:
+        return
+
+    menu_plots(df)
+
+    print("\nPreparing features...")
+    X, y, numeric_features, le_target = prepare_features(df, target_col="Class")
+    print(f"Prepared features: {numeric_features}")
+
+    print("\nTraining and evaluating models...")
+    results, X_train, X_test, y_train, y_test = train_and_evaluate(X, y)
+    print("Training complete.\n")
+
+    save_data(df)
+
+    best_name = max(results, key=lambda k: results[k]["test_acc"])
+    best_pipeline = results[best_name]["pipeline"]
+    print(f"Best model: {best_name} (test_acc={results[best_name]['test_acc']:.3f})")
+    save_model_artifacts(best_pipeline, le_target, model_name=f"{best_name}.joblib")
+
+    if input("\nDo you want to test a custom input interactively? (y/n): ").lower().startswith("y"):
+
+        pipeline, le = load_model_artifacts(f"{best_name}.joblib")
+        predict_from_input(pipeline, numeric_features, le)
+
+    print("\nAll done.")
+
+
+if __name__ == "__main__":
+    main()

requirements.txt

@@ -3,5 +3,6 @@ seaborn
 matplotlib
 scikit-learn
 numpy
+joblib
 
 #to run: pip install -r requirements.txt