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neuralprophet with spliting
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| ### with spliting train and test | |
| m = NeuralProphet() | |
| df_train, df_test = m.split_df(apple_features, valid_p=0.3,freq='D') | |
| # fiting | |
| metricss = m.fit(df_train, validation_df=df_test , freq='D',epochs=1000) | |
| # predict | |
| forecast = model.predict(df_train) | |
| # plot | |
| fig_forecast = model.plot(forecast) |
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