I'm making a churn model.
My observation window (historic data) length is 3 weeks. There are some users that are not been registered to the app that I'm analyzing for three weeks, and as a result, I don't have data for their behavior before two or three weeks.
One solution that I thought of is to initialize with zeros all the features for users with not enough data history. For example, if a user visited the app 10 times in the last week, and registered just before a week, the feature value for "visit_week_0" will be 10, and the values for the features: "visit_week_1" and "visit_week_2" will be 0. I am not sure this solution is good enough, because in this way the model can't distinguish between a user that has 3 weeks of history and literally visit the app 0 times and between a user that was registered to the app just a week or two ago.
I'm thinking about initializing these features with zeros for new users, but adding a binary feature that marks if the user was registered this week or not.
Another solution that I thought of is not to initialize features with zeros for new users, but instead to fill them with the mean values of the other users.
What do you think is a better approach?