I am an ml noob. I have a task at hand of predicting click probability given user information like city, state, os version, os family, device, browser family browser version, city, etc. I have been recommended to try logit since logit seems to be what MS and Google are using too. I have some questions regarding logistic regression like:
Click and non click is a very very unbalanced class and the simple glm predictions do not look good. How to make the data work through this?
All variables I have are categorical and things like device and city can be numerous. Also the frequency of occurrence of some devices or some cities can be very very low. So how to deal with what I can say is a very random variety of categorical variables?
One of the variables that we get is device id also. This is a very unique feature that can be translated to a user's identity. How to make use of it in logit, or should it be used in a completely different model based on a user identity?