image_output_1.png
from sklearn.linear_model import LinearRegression

from sklearn.metrics import mean_squared_error

import numpy as np # linear algebra

from sklearn.linear_model import LinearRegression
lr = LinearRegression()
lr.fit(X_train, Y_train)

predictions = lr.predict(X_test)

from sklearn.metrics import mean_squared_error
mse = mean_squared_error(Y_test, predictions)
rmse = np.sqrt(mse)
print(rmse)