<gh_stars>0
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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',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



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import matplotlib.pyplot as plt

# code starts here        
cols=X_train.columns
fig ,axes =plt.subplots(3,3,figsize=(20,8))
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 her
corr=X_train.corr()
print(corr)
X_train.drop(['play_star_rating','val_star_rating'], 1 ,inplace=True)
X_test.drop(['play_star_rating','val_star_rating'], 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)



# Code ends here



# --------------
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',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(3,3,figsize=(20,8))
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 her
corr=X_train.corr()
print(corr)
X_train.drop(['play_star_rating','val_star_rating'], 1 ,inplace=True)
X_test.drop(['play_star_rating','val_star_rating'], 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)



# Code ends here
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