I have a couple of hundred categories where each of these categories has a specific set of attributes having different values (historical).
The problem I need to solve is to select the best set of categories out of a smaller group which meet some constraints.
I'm new to datascience and was wondering how do I go about solving this problem?
One option I thought was to use multiple regression for the different attributes to assign a weight to each category and then use these to generate a random forest on the historical groups of categories to train and test them.
Does this make sense?