#I've implemented MLR using Stochastic Gradient Descent Optimization Technique on the same "data.csv" file to predict price of house. import numpy as np import pandas as pd import matplotlib.pyplot as plt from sklearn.linear_model import LinearRegression from sklearn.linear_model import SGDRegressor from sklearn.cross_validation import train_test_split from sklearn.metrics import r2_score data = pd.read_csv('E:\data.csv', header=None, names=['Size', 'bed', 'profit']) X = data.as_matrix(columns=["Size", "bed"]) Y = data['profit'].tolist() X_train, x_test, Y_train, y_test = train_test_split(X, Y, test_size=0.2, random_state=0) fitter = SGDRegressor(loss="squared_loss", penalty=None) fitter.max_iter = np.ceil(10**5 / len(Y_train)) fitter.fit(X_train, Y_train) y_pred = fitter.predict(x_test) from sklearn.metrics import mean_squared_error rmse = np.sqrt(mean_squared_error(y_test, y_pred)) r2 = r2_score(y_pred, y_test)