from sklearn.metrics import mean_squared_error, r2_score

# Evaluate model performance
y_pred = model.predict(X_test)

rmse = np.sqrt(mean_squared_error(y_test, y_pred))
r2 = r2_score(y_test, y_pred)

print(f"RMSE: {rmse:.2f}")  # Lower is better
print(f"R²: {r2:.2f}")     # Higher is better (max 1.0)