I am trying to predict the length of time (not a time series!) a work task takes, especially technical changes. The output should be a prediction of the duration the task will take in days. As input I have the historical data with characteristics like what kind of change, priorisation, responsibles, components, categorie, how many tasks, cost relevance, ..and some more.. and of course the duration the change lasted.
So after I prepared the data, I am thinking what would be the best way to solve this with machine learning. I thought about linear regression or should I first do a clustering and then regression? My thoughts relating to the clustering were that I can first characterize similar tasks and then do the regression. Or would a multiple regression be the better way? Do you have any suggestions based on your experiences?