Linear Regression with sklearn – cheat sheet

# import and instantiate model
from sklearn.linear_model import LinearRegression
model = LinearRegression()

#prepare test data
features_train = df_train.loc[:, 'feature_name']
target_train = df_train.loc[:, 'target_name']

#fit (train) model and print coefficient and intercept
model.fit(features_train , target_train )
print(model.coef_)
print(model.intercept_)

# calculate model quality
from sklearn.metrics import mean_squared_error
from sklearn.metrics import r2_score

target_prediction = model.predict(features_train)
print(mean_squared_error(target_train , target_prediction))
print(r2_score(target_train , target_prediction))

# test predictions
features_test = df_train.loc[:, 'feature_name'] 
target_test = df_train.loc[:, 'target_name']
target_prediction_test = model.predict(features_test) 
print(mean_squared_error(target_test, target_prediction_test )) 
print(r2_score(target_test, target_prediction_test ))