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from sklearn.linear_model import LinearRegression

from sklearn.linear_model import LinearRegression

from sklearn.linear_model import LinearRegression
regressor1 = LinearRegression()
regressor1.fit(X_train1, y_train1)

y_pred1 = regressor1.predict(X_test1)

print(mean_squared_error(y_test1, y_pred1), explained_variance_score(y_test1, y_pred1), max_error(y_test1, y_pred1), mean_absolute_error(y_test1, y_pred1), mean_squared_error(y_test1, y_pred1), mean_squared_log_error(y_test1, y_pred1), median_absolute_error(y_test1, y_pred1), r2_score(y_test1, y_pred1), mean_poisson_deviance(y_test1, y_pred1), mean_gamma_deviance(y_test1, y_pred1), mean_tweedie_deviance(y_test1, y_pred1, power=1))