# --------------
import pandas as pd
import numpy as np
from sklearn.cross_validation import train_test_split
# code starts here
df = pd.read_csv(path)
print(df.head())

X = df.drop('list_price',axis=1)
y = pd.Series(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(3,3, figsize=(30,30))

for i in range(3):
    for j in range(3):
        axes[i,j].set_title(col)
        col = cols[i*3+j]
        axes[i,j].scatter(X_train[col],y_train)
        axes[i,j].set_xlabel(col)
        axes[i,j].set_ylabel('list_price')
plt.show()
# code ends here



# --------------
# Code starts here
corr = abs(X_train.corr())
print(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("mean squared error :",mse)

r2 = r2_score(y_test,y_pred)
print("r^2 score :",r2)

# Code ends here


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

residual = abs(y_test - y_pred)

plt.hist(residual, bins=25)

# Code ends here


