Hi, and welcome!

It's worth getting a copy of *Applied Logistic Regression 3rd Edition* by David W. Hosmer Jr., Stanley Lemeshow and Rodney X. Sturdivant (2009), especially since you have a stats background. Unfortunately, it's code agnostic, with no examples in any language. I'm working to remedy that in `R`

*see, e.g.*, but it's slow going.

The rule of thumb for categorical variables is to treat them as continuous if there are more than a dozen or so, and to create dummy binary variables if they are not.

For example, if a variable can take on one of three values, say, *red, yellow, blue* you would create three substitute binary variables of those names.

The risk metric that comes out of a logistic regression is the `odds ratio`

, which is just what it sounds like. An odds ratio of `0`

means that the outcome, Y is equally likely with or without the independent variables X_i ...X_n. OR > 0 means more likely, 1\frac 1 2 one and a half times more likely, -\frac 1 2, half as likely, etc.

If you have enough historical data, you'll want to partition it into a training set and validation set and use the goodness of fit tests to see how well the model does in practice.

Come on back when you have specific questions, and please see the FAQ: What's a reproducible example (`reprex`) and how do I do one? on how to attract good answers.