predict_house_price.py

from sklearn.datasets import fetch_california_housing
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error
from sklearn.metrics import r2_score


def predict_house_price():
    data = fetch_california_housing(as_frame=True)
    x = data['data']
    y = data['target']

    x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.25)

    model = LinearRegression()
    model.fit(x_train, y_train)

    pred = model.predict(x_test)

    mse = mean_squared_error(y_test, pred) ** 0.5
    r2 = r2_score(y_test, pred)
    print('--------------------------------')
    print(f'MSE: {round(mse, 2)}')
    print(f'R2: {round(r2, 2)}')
    print('--------------------------------')


if __name__ == '__main__':
    predict_house_price()