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from sklearn.linear_model import LinearRegression

from sklearn.metrics import mean_squared_error

model = LinearRegression(n_iters=10000)
model.fit(X_train, y_train)
MSE = mean_squared_error(y_test, model.predict(X_test))
print('MSE: {}'.format(MSE))