So, I have 8 columns in my dataframe: 5 features and other 3 are targets. After following these process, the results obtained are not good. Can anyone provide any feedback in the steps followed?
Here I am defining 2 minmax scaling variables, one for features and other for targets columns. Once model predicts the values, we run reverse scaling on features and predicted targets again to obtain the results.
#smoothening and minMax scaling
smoother=tsmoothie.KalmanSmoother(component='level_trend', component_noise={'level':0.1, 'trend':0.1})
scaler_features = MinMaxScaler(feature_range=(0,1))
scaler_targets = MinMaxScaler(feature_range=(0,1))
#setting up features and targets from the df
df_norm_feature = scaler_features.fit_transform(raw_df.iloc[:,:5])
df_norm_target = scaler_targets.fit_transform(raw_df.iloc[:,5:])
#smoothening features and targets
smoother.smooth(df_norm_feature)
smoothed_features = smoother.smooth_data
smoother.smooth(df_norm_target)
smoothed_targets = smoother.smooth_data
#split into train test and train the data, and prepare the model on train.
#for reverse transformation I am using the following code.
test_resultsForAll = mode.predict(test_data)
transformed_test_resultsForAll = scaler_targets.inverse_transform(test_resultsForAll))
but the results obtained via this method are not good. Are there any mistakes in the order of steps or do I need to perform minMax scaling & smoothening on the whole dataset at once?