fdd9.py

elastic_net_parag_grid =  {
                'alpha'     : [0.1,1,10,0.01,0.01,5],
                'l1_ratio'  :  np.arange(0.40,1.00,0.10),
            }
elastic_net_regressor = ElasticNet()
elastic_net_grid_search = GridSearchCV(
    elastic_net_regressor,
    param_grid=elastic_net_parag_grid,
    cv = 5,
    n_jobs=-1,
    verbose=1
)
elastic_net_grid_search.fit(encoded_X_train,y_train)
preds = elastic_net_grid_search.best_estimator_.predict(encoded_X_valid)
final_test_score = np.sqrt(mean_squared_error(y_valid, preds))

elastic_net_grid_search.best_params_

######################################################
{'alpha': 5, 'l1_ratio': 0.6}