# --------------
import pandas as pd
import numpy as np
from sklearn.cross_validation import train_test_split
# code starts here
df=pd.read_csv(path)
X=pd.read_csv(path)
y=df['list_price']

X.drop('list_price',axis=1,inplace=True)
X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=.3,random_state=6)
# code ends here
df.head()


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

# code starts here        
cols=X_train.columns
fig = plt.figure()
axes = fig.subplots(nrows=3, ncols=3)

n=np.size(X_train)
for i in range(0,3):
    for j in range(0,3):
        col=cols[i*3+j]
        axes[i,j].scatter(X_train[col],y_train)


# code ends here



# --------------
# Code starts here

corr=X_train.corr()
X_train.drop(['play_star_rating','val_star_rating'],axis=1,inplace=True)
X_test.drop(['play_star_rating','val_star_rating'],axis=1,inplace=True)
# 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)
print(mse)
r2=r2_score(y_test,y_pred)
print(r2)
# Code ends here


# --------------
# Code starts here

residual=y_test-y_pred
plt.hist(residual)
plt.show()

# Code ends here


