When to build separate models 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)
short answer: assess simple models first, and then build more complex ones if necessary.

For a roadmap, I'd go like this:


get familiar with the different categories of machine learning problems: classification vs regression problems. Your problem falls in the regression category (at least at first sight, you could also reframe it into a classification problem if necessary)
get familiar with the main corresponding algorithms
get familiar with the strategies to train / test a model performance
since you are explicitly asking for online resources, you can have a look at Andrew Ng's machine learning course (especially the first weeks of the course). It's not free, but you can also have a general overview buy looking for his Machine Learning Yearning Book. Another resource coming to my mind is Machine Learning Mastery website (can't remember the guy's name) where you can find some introductory ebooks to answer those questions. It's well written and can provide you a good lift


Does this help?