sklearn simple linear regression

 

Import Datasets sklearn from 
from sklearn.model_selection Import train_test_split 
from sklearn.linear_model Import LinearRegression 
from sklearn.metrics Import mean_squared_error 

# load data rates Boston 
Boston datasets.load_boston = () 
X, Y = boston.data, boston.target 

# training set is divided and test set 
x_train, x_test, y_train, android.permission.FACTOR. = train_test_split (the X-, the y-, test_size = 0.25, random_state = 10010) 

# training set to train the model 
REG = LinearRegression () 
reg.fit (x_train, y_train) 

# using the model to predict 
y_predict reg.predict = (x_test) 

# between the predicted value and the true value of the mean square error calculation model the MSE 
Print (mean_squared_error (android.permission.FACTOR., y_predict))