Best modeling technique for predicting waiting times? 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. 
 A: Waiting times can often be modelled by the exponential distribution, but this better describes the time between two events of the same type, e.g. it would probably nicely model the time between two subsequent questions. 
The time to answer has some more complex properties:


*

*There is a real chance of it never being answered

*There will be some delay before an answer can realistically appear

*Complex questions take longer (maybe approximately captured by #characters in question?)


For these reasons the exponential distribution won't be a perfect fit. The point about the delay is the most serious defect. 
Survival analysis might be another option, but also hasn't got the "waiting" time for people to read and understand the question, before they can answer. 
Other concerns include dependence on time of day and day of week. There could be spam bots that answers the question very quickly.
Since this is exploratory anyway, I would start by plotting the data. Start with some histograms of the time until an answer, look at how many never get answered. Go the same plots by time of day and day of week. See if there are differences. Then plan from there.
A: I have accepted an answer above and am only writing this as an answer because I think my research could be beneficial to other Python users. 
The comments and answers above helpfully directed me towards Survival Analysis. After exploring the modelling options in StatsModels, I then discovered the library Lifelines by Cameron Davidson Pilon (@Cam.Davidson.Pilon). 
I can thoroughly recommend Lifelines to anyone looking to do duration-based analysis in Python. It's excellent in many ways. I particularly like the way it addresses - both theoretically and practically -  the concept of Censorship. Given how elegant and effective the library is, and how broad the applications of Survival Analysis are, I'm somewhat surprised Lifelines isn't better known.
I can also recommend watching this introductory talk. It got me up and running with Lifelines in no time.    
