# --------------
import pandas as pd
import numpy as np
from sklearn.cross_validation import train_test_split
# code starts here
df=pd.read_csv(path)
df.head(5)
X=df.drop(['list_price'],axis=1)
y=df['list_price']
X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.3,random_state=6)
# code ends here



# --------------
import matplotlib.pyplot as plt

# code starts here        
cols = X_train.columns

fig, axes = plt.subplots(nrows=3, ncols=3, figsize=(20,20))

for i in range(0,3):
    for j in range(0,3): 
            col = cols[i*3 + j]
            axes[i,j].set_title(col)
            axes[i,j].scatter(X_train[col],y_train)
            axes[i,j].set_xlabel(col)
            axes[i,j].set_ylabel('list_price')
        

# code ends here
plt.show()


# --------------
# Code starts here
corr=X_train.corr(method ='pearson') 
print(corr)
X_train.drop(['play_star_rating','val_star_rating'],inplace=True,axis=1)
X_test.drop(['play_star_rating','val_star_rating'],inplace=True,axis=1)
# Code ends here


# --------------
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error, r2_score

# Code starts here
regressor=LinearRegression()
regressor.fit(X_train,y_train)
y_pred=regressor.predict(X_test)
mse=mean_squared_error(y_test,y_pred)
r2=r2_score(y_test,y_pred)
print(mse,r2)

# Code ends here


# --------------
# Code starts here
residual=abs(y_pred-y_test)



# Code ends here


