# SVR regressor fitting iterated.
from sklearn.svm import SVR
from sklearn.metrics import mean_squared_error
svr_rmse = []
for i in range(10):
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.15)
    regressor = SVR(kernel = 'rbf', C = 10)
    regressor.fit(X_train, y_train)
    y_pred = regressor.predict(X_test)
    rmse = (mean_squared_error(y_test, y_pred)) ** 0.5
    svr_rmse.append(rmse)

# Decision tree regressor fitting iterated.
from sklearn.tree import DecisionTreeRegressor
dectree_rmse = []
for i in range(10):
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.15)
    regressor = DecisionTreeRegressor()
    regressor.fit(X_train, y_train)
    y_pred = regressor.predict(X_test)
    rmse = (mean_squared_error(y_test, y_pred)) ** 0.5
    dectree_rmse.append(rmse)

# Linear regressor fitting iterated.
from sklearn.linear_model import LinearRegression
linreg_rmse = []
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)