housing-boston.py

#load dataset
import pandas as pd
housing = pd.read_csv('https://storage.googleapis.com/dqlab-dataset/pythonTutorial/housing_boston.csv')
#Data rescaling
from sklearn import preprocessing

data_scaler = preprocessing.MinMaxScaler()
housing[['RM','LSTAT','PTRATIO','MEDV']] = data_scaler.fit_transform(housing[['RM','LSTAT','PTRATIO','MEDV']])
# getting dependent and independent variables
X = housing.drop(['MEDV'], axis=1)
y = housing['MEDV']
# checking the shapes
print('Shape of X:', X.shape)
print('Shape of y:', y.shape)

# splitting the data
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, random_state = 0)
# checking the shapes  
print('Shape of X_train :', X_train.shape)
print('Shape of y_train :', y_train.shape)
print('Shape of X_test :', X_test.shape)
print('Shape of y_test :', y_test.shape)

##import regressor from Scikit-Learn
from sklearn.linear_model import LinearRegression 
# Call the regressor
reg = LinearRegression()
# Fit the regressor to the training data
reg = reg.fit(X_train, y_train)
# Apply the regressor/model to the test data
y_pred = reg.predict(X_test)
print(y_pred.shape)

from sklearn.metrics import mean_squared_error, mean_absolute_error   
import numpy as np
import matplotlib.pyplot as plt

#Calculating MSE, lower the value better it is. 0 means perfect prediction
mse = mean_squared_error(y_test, y_pred)
print('Mean squared error of testing set:', mse)
#Calculating MAE
mae = mean_absolute_error(y_test, y_pred)
print('Mean absolute error of testing set:', mae)
#Calculating RMSE
rmse = np.sqrt(mse)
print('Root Mean Squared Error of testing set:', rmse)

#Plotting y_test dan y_pred
plt.scatter(y_test, y_pred, c = 'green')
plt.xlabel('Price Actual')
plt.ylabel('Predicted value')
plt.title('True value vs predicted value : Linear Regression')
plt.show()