I am attempting to build a model that predicts the likelihood of 1000 customers churning every week, for the next 5 week. My training consists of 4 continuous feature variables, and a class variable that represents whether or not a customer churned in the upcoming week, which Churn being defined as cancellation of service.
'data.frame': 1000 obs. of 5 variables:
$ ID : chr "9722209" "9722213" "9722215" "9722223" ...
$ feat.1 : num 2 5 1 2 7 2 0 5 2 2 ...
$ feat.2 : num 3 2 2 3 1 2 2 6 4 9 ...
$ feat.3 : num 9 4 1 2 2 8 2 2 2 2 ...
$ feat.4 : num 2 0 0 0 2 5 4 2 0 0 ...
$ churn.7.days : num 1 0 1 0 0 0 0 0 0 0 ...
My question is about this: how can I use this data set to predict the likelihood of churn for not only the next week (which is relatively straight forward), but for the next subsequent 8 weeks?