lr_sklearn.py

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn import datasets
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score
from scipy.optimize import minimize

boston = datasets.load_boston()
y = boston.target
X = pd.DataFrame(boston.data, columns = boston.feature_names)

ss = StandardScaler()
Xs = ss.fit_transform(X)
Xs = pd.DataFrame(Xs, columns=boston.feature_names)
Xs['const'] = 1
Xss = Xs.copy()

lr = LinearRegression(fit_intercept=False)
lr.fit(Xss, y)
pd.DataFrame(list(lr.coef_), index=Xs.columns, columns=['coefs'])

y_pred = lr.predict(Xss)
print('Means Squared Error: ', mean_squared_error(y, y_pred))
print('R-Square: ', r2_score(y, y_pred))

plt.scatter(lr.predict(Xss), y, color = "red")
plt.scatter(lr.predict(Xss), lr.predict(Xss), color = "green")
plt.title("Predicted vs Actual Price")
plt.xlabel("Boston House Price(Predicted)")
plt.ylabel("House Price")
plt.show()