I'm doing some data prep on a dataset provided by a telecommunication company. There is a continuous variable that indicates how many months have passed since a customer renewed her contract. However, 20% of observations have missing values. I figured out that there are two reasons why a value might be missing:
1- a customer did not renew the contract
2- a customer is still on her first contract and therefore never had the chance to renew it.
What I want to do is to fill the missing values with respectively "Not renewed" and "Not applicable", and then discretize the rest of the numerical values through a supervised algorithm (chi2, MDLP) so that the variable would turn into a categorical one.
In the end, for this variable I'd have some observations categorized through simple labeling, while others through a supervised discretization algorithm.
Question: is it correct to do so? If not, how should I handle the problem?
Please see my comment below for additional information.