Question: In [57]: 1 Result_df4=pd. DataFrame ( {Alpha : , RMSE_Train: , RMSE_Test: }) In [58]: for i in np. arange (0.0, 1,0.1): model_SES_alpha_i=Model_SES. fit (smoothing_level=i,

$In [57]: 1 Result_df4=pd. DataFrame ( {"Alpha" : , "RMSE_Train": ,$

In [57]: 1 Result_df4=pd. DataFrame ( {"Alpha" : , "RMSE_Train": , "RMSE_Test": }) In [58]: for i in np. arange (0.0, 1,0.1): model_SES_alpha_i=Model_SES. fit (smoothing_level=i, optimized=False, use_brute=True) 5ES_train["predict", il=model_SES_alpha_i. fittedvalues 5ES_test ["predict", il=model_SES_alpha_i. forecast(steps=56) rmse_model4_train=metrics. mean_squared_error (SES_train[ "Sparkling"],SES_train["pred rmse_model4_test=metrics . mean_squared_error ($85_test["Sparkling"],SES_test["predict 8 9 Result_df4=Result_df4. append( {"Alpha" : [i], "RMSE_Train": [rmse_model4_train], "RMSE_Te 10 11 Result_df4 12 Out [58]

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