I have a data set of recruiting pipeline information that has 12 columns of categorical variables (they range from binary variables like gender to non-binary variables like the name of the school the applicant attended). The last column (13) contains a "Yes" or "No" value that tells if the person is still in the recruiting process.
The idea here is to build a model that can predict (reasonably well) the likelihood of the person withdrawing from the recruiting process at any given stage (there are many stages and this field is one of the 12 independent variables captured).
I was thinking of using logistic regression to create the model but all the predictor variables are categorical, which I hear, doesn't bode well for logistic regression. Another factor is that some fields have missing values (like gender) that I cannot reasonably input/capture accurate data for atm.
Given my situation, what approach do you think would be a good starting point?
Was thinking of random forest but not sure if there's a better way to go about tackling this problem.