Implement Regularized Regression for house price prediction and evaluate there accuracy using sckitlearn. • Ridge Regression • Lasso Regression

def get_regressor(name):
    if name == 'ridge':
        return Ridge()
    elif name == 'lasso':
        return Lasso()
    else:
        return None
    
regressors = ['ridge', 'lasso']
for reg_name in regressors:
    regressor = get_regressor(reg_name)
    regressor.fit(X_train, y_train)
    y_pred = regressor.predict(X_test)
    print('Regressor: {}'.format(reg_name))
    print('R^2: {}'.format(r2_score(y_test, y_pred)))
    print('RMSE: {}'.format(np.sqrt(mean_squared_error(y_test, y_pred))))
    print()