import matplotlib.pyplot as plt 
import numpy as np 
from sklearn import datasets, linear_model, metrics 
import pandas as pd

  
# load the boston dataset 
df = pd.read_csv("finalEncoded.csv")
y = df['price']
X = df.drop(columns=['price'])
  
# splitting X and y into training and testing sets 
from sklearn.model_selection import train_test_split 
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4, 
                                                    random_state=1) 
  
# create linear regression object 
reg = linear_model.LinearRegression() 
  
# train the model using the training sets 
reg.fit(X_train, y_train) 

y_pred = reg.predict(X_test)
diff = abs(y_pred - y_test)

print(sum(diff)/len(diff))
print(reg.score(X_train, y_train))
  
# regression coefficients 
print('Coefficients: \n', reg.coef_) 
  
# variance score: 1 means perfect prediction 
print('Variance score: {}'.format(reg.score(X_test, y_test))) 
  
# plot for residual error 
  
## setting plot style 
plt.style.use('fivethirtyeight') 
  
## plotting residual errors in training data 
plt.scatter(reg.predict(X_train), reg.predict(X_train) - y_train, 
            color = "green", s = 10, label = 'Train data') 
  
## plotting residual errors in test data 
plt.scatter(reg.predict(X_test), reg.predict(X_test) - y_test, 
            color = "blue", s = 10, label = 'Test data') 
  
## plotting line for zero residual error 
plt.hlines(y = 0, xmin = 0, xmax = 50, linewidth = 2) 
  
## plotting legend 
plt.legend(loc = 'upper right') 
  
## plot title 
plt.title("Residual errors") 

plt.xlim(-15000,50000)
plt.ylim(-40000, 40000)
  
## function to show plot 
plt.show() 