# Re-iterate the linear regression model, this time creating a list of features and their corresponding coefficients.
from sklearn.linear_model import LinearRegression
linreg_rmse = []
coef_list = []
for i in range(10):
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.15)
    regressor = LinearRegression()
    regressor.fit(X_train, y_train)
    y_pred = regressor.predict(X_test)
    rmse = (mean_squared_error(y_test, y_pred)) ** 0.5
    linreg_rmse.append(rmse)
    coef = regressor.coef_
    coef_list.append(coef)
features = tv.get_feature_names()
features = pd.DataFrame(features)
coef_list = pd.DataFrame(coef_list)
coef_list = coef_list.transpose()
coef_features = pd.concat([features, coef_list], axis = 1, sort = False)
coef_features.columns = ['Feature', 'Iteration 1', 'Iteration 2', 'Iteration 3', 'Iteration 4', 'Iteration 5', 'Iteration 6', 'Iteration 7', 'Iteration 8', 'Iteration 9', 'Iteration 10']