Queries regarding feature importance for categorical features:
Context: I have almost 185 categorical features and these categorical features have either 2 or 3 or 8 or 1 or sometimes 4 categories, null's also. I need to select top 60 features for my model. I also understand that features needs to be selected based on business importance OR feature importance by random forest / decision tree.
I have plotted histograms for each feature (value count vs category) to analyse. What is the approach to select whether feature is important?
What is the standard practice followed across data science industry to get the feature importance from categorical data?
Is there basic and elegant way to select the top important features?
How do I engineer these categorical features?