I'm exploring some Stack Overflow data. Amongst other variables, I have variables for the time questions were asked and the time they were answered.
I'm interested in predicting how long a questioner might expect to wait before their question is answered, based on the programming language.
I have 26k observations divided between 10 languages. Format-wise, I can format the time differentials between question asked and answered as integers, and group the data by programming language as neccesary.
The output of my model would ideally be very simple: just an integer/float representing the number in minutes a user might typically expect to wait before their question is answered.
What would be the most suitable modelling / machine learning technique for this (ideally in Python)? I've explored various GLM types in StatsModels but can't find something that's clearly suitable. As the data are neither continuous nor linear I don't think OLS is right. The data's obviously not binary either, so logistic regression's out. As I'm just dealing with time differentials as integers, I don't think this requires a Time Series analysis model either.
For the record this is just a personal project based on a publicly available Stack Overflow data dump. I have no affiliation with Stack Overflow.