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from sklearn.pipeline import Pipeline from sklearn.compose import ColumnTransformer from sklearn.preprocessing import StandardScaler, OneHotEncoder from sklearn.ensemble import GradientBoostingClassifier from sklearn.model_selection import cross_val_score import joblib numeric_features = ['age', 'income', 'tenure'] categorical_features = ['country', 'plan'] preprocessor = ColumnTransformer([ ('num', StandardScaler(), numeric_features), ('cat', OneHotEncoder(handle_unknown='ignore'), categorical_features), ]) pipeline = Pipeline([ ('preprocessor', preprocessor), ('classifier', GradientBoostingClassifier(n_estimators=200, max_depth=4)), ]) scores = cross_val_score(pipeline, X_train, y_train, cv=5, scoring='roc_auc') print(f"AUC: {scores.mean():.3f} ± {scores.std():.3f}") pipeline.fit(X_train, y_train) joblib.dump(pipeline, 'churn_model.pkl')