I'm pretty new to predictive modeling, but am interested in generating predictions for credit card account spend. These are existing accounts.
The data I have available to me is Card Type (i.e. Platinum, Black, Gold) and spend/transaction data over the last year.
I have a two questions:
How do I decide whether to use linear regression or a decision tree/random forest? Or do I try many techniques?
I expect spend will differ considerably across Card Types; does this suggest I should build separate models by Card Types? If I had a another set of attributes that I thought might segment customers, but wasn't sure, how would I evaluate that?
If there are guides online that answer the above, that'd be much appreciated as well. Haven't found any good ones (or at least I think not)