I am trying to design an algorithm that takes in a new user with the variables
department, location, job_role etc. and I want a machine-learning algorithm to decide what software and hardware this new user would need.
I am rattling my brain thinking how I could get this to work - I could use a supervised learning approach and train a model with a dataset of already employed users and the software and hardware they use, however, the variables in this dataset would be extensive as, through one-hot encoding, each user object would have to have variables that represent each possible software or hardware a user could own. Or can my dataset have a collection of user objects but not each object has the same software/hardware variables?
I'm very confused about how to go about this so any insight would help. I'm probably looking at going about this the wrong way.