I am new to machine learning. 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, etc. I have been advised to try logit since logit seems to be what MS and Google are using. I have some questions regarding logistic regression:
Click and non click is a very very unbalanced class and the simple GLM predictions do not look good. How can I make the data work better with the GLM?
All the 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. How can I deal with this distribution of categorical variables?
One of the variables that we get is device ID. This is a very unique feature that can be translated to a user's identity. How can I make use of it in logit, or should it be used in a completely different model based on user identity?