# Required libraries
import pandas as pd
import numpy as np
from sklearn.datasets import load_boston
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
boston = load_boston()
print(boston.data.shape)
print(boston.feature_names)
print(boston.target.shape)
# Creating Pandas dataframe
bos = pd.DataFrame(boston.data)
print(bos.head())
bos['PRICE'] = boston.target
X = bos.drop('PRICE', axis = 1)
Y = bos['PRICE']
Y
# Test and train are two random subsets.Split data into these two subsets.
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.33, random_state = 5)
print(X_train.shape)
print(X_test.shape)
print(Y_train.shape)
print(Y_test.shape)
# systamization
from sklearn.preprocessing import StandardScaler
std = StandardScaler()
X_train = std.fit_transform(X_train)
X_test = std.fit_transform(X_test)
from sklearn.linear_model import SGDRegressor
from sklearn.metrics import mean_squared_error, r2_score
clf = SGDRegressor()
clf.fit(X_train, Y_train)
Y_pred = clf.predict(X_test)
print("Coefficients: \n", clf.coef_)
print("Y_intercept", clf.intercept_)

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