Kaggle competition: M5 Forecasting - Accuracy
logreg = LogisticRegression(random_state=1)
 logreg.fit(X_train, Y_train)
 acc_train_lr = np.sqrt(mean_squared_log_error(Y_train, logreg.predict(X_train)))
 acc_eval_lr = np.sqrt(mean_squared_log_error(Y_eval, logreg.predict(X_eval)))
 print(f'LR: Train = {acc_train_lr} Evaluate = {acc_eval_lr}' )<train_on_grid>
poisson = pd.read_csv('/kaggle/input/m5-first-public-notebook-under-0-50/submission.csv')
 tweedie = pd.read_csv('/kaggle/input/m5-accuracy-tweedie-is-back/submission.csv' )
<compute_train_metric><EOS> 
poisson = poisson.sort_values(by = 'id' ).reset_index(drop = True)
 tweedie = tweedie.sort_values(by = 'id' ).reset_index(drop = True)
 sub = poisson.copy()
for i in sub.columns :
if i != 'id' :
sub[i] = 0.35*poisson[i] + 0.65*tweedie[i]
sub.to_csv('submission.csv', index = False )