programming-tutorials/multiple_linear_regression.py at master · amanbhandari2002/programming-tutorials · GitHub

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import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.datasets import load_boston
from sklearn.metrics import mean_squared_error, r2_score
from sklearn.linear_model import LinearRegression

bh_data = load_boston()

print(bh_data.keys())

boston = pd.DataFrame(bh_data.data, columns=bh_data.feature_names)

print(bh_data.DESCR)

boston['MEDV'] = bh_data.target

X = pd.DataFrame(np.c_[boston['LSTAT'], boston['RM']], columns=['LSTAT','RM'])
y = boston['MEDV']

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state=9)

lin_reg_mod = LinearRegression()

lin_reg_mod.fit(X_train, y_train)

pred = lin_reg_mod.predict(X_test)

test_set_rmse = (np.sqrt(mean_squared_error(y_test, pred)))

test_set_r2 = r2_score(y_test, pred)

print(test_set_rmse)
print(test_set_r2)