# Likelihood of churn modeling

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?

• Welcome to CV! Can you define "churn" for us? You can edit your question to make that clear by clicking the "edit" link in the lower left. – Alexis Apr 12 '18 at 20:49
• Thank you, it's good to be here! I have edited the definition of churn above. – ari8888 Apr 12 '18 at 20:55
• Is time a variable in this data set? – Alexis Apr 12 '18 at 22:11
• It isn't. The only time information we have is within our class variable, which is the event of cancellation within the upcoming week... – ari8888 Apr 12 '18 at 23:02