# --------------
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()
X = df.drop(['list_price'],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)
for i in range(3):
    for j in range(3):
        col = cols[i*3+j]
        axes[i][j] = plt.scatter(X_train[col],y_train)

# code ends here



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


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

# Code starts here
regressor = LinearRegression()
y_pred = regressor.fit(X_train,y_train).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 = np.abs(y_pred-y_test)
residual.hist()


# Code ends here


