<gh_stars>0
# --------------
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.iloc[:,[0,2,3,4,5,6,7,8,9]]
y = df.iloc[:,1]

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(0,3): # iterate through rows
    for j in range(0,3):  # iterate through columns
        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(columns=['play_star_rating', 'val_star_rating'], inplace=True)
X_test.drop(columns=['play_star_rating', 'val_star_rating'], 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)
r2 = r2_score(y_test, y_pred)

print(mse)
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)
print(df.head())

X = df.iloc[:,[0,2,3,4,5,6,7,8,9]]
y = df.iloc[:,1]

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(0,3): # iterate through rows
    for j in range(0,3):  # iterate through columns
        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(columns=['play_star_rating', 'val_star_rating'], inplace=True)
X_test.drop(columns=['play_star_rating', 'val_star_rating'], 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)
r2 = r2_score(y_test, y_pred)

print(mse)
print(r2)
# Code ends here


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

residual = y_test - y_pred
plt.hist(residual)

# Code ends here
# -------------- # code starts here # code ends here # -------------- # code starts here # iterate through rows # iterate through columns # code ends here # -------------- # Code starts here # Code ends here # -------------- # Code starts here # Code ends here # -------------- # Code starts here # Code ends here