I am working on developing a predictive model using Random Forest. There are a lot of users that log in to the site but only a fraction of them actually monetize on that day. I am trying to predict the probability of monetization of a user during a login. The predictor variables have been selected. Now the issue is that one user can log in on multiple days and have different values of predictor variables and the label (Monetized/ Not Monetized) for each log in.
If I try to train the data on the last 30 days' data, some users would occur multiple times and others would only occur one time. This may lead to a higher weight-age being given to the users who logged in on more days.
How can I ensure that equal weightage is assigned to all the users even though the users might have different numbers of logins and hence different counts of data points.