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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')