Having some problems understanding how to tackle my problem in general, i.e. not specifically what algorithm to go for. I'm familiar with deep learning techniques, boosting, etc. so my core problem isn't algorithms, my problem is setting up the problem and my target logically.
I have a dataset showing a test that was conducted on people eating one of two types of food and their stats along with it.
- Every row represent a person
- None of the people had tried any of the two food types before the test
- I have some metrics columns describing my target: how much energy and how much sleep a person gets. Both of these columns exists as accumulated numbers before the test but also after the test.
- I have several numerical and categorical features describing the profile of the person.
What is interesting about the test was that it was not conducted using a random trial, but instead, every person was recommended one of the two food types based on the amount of sleep that person had prior to the test (one of the columns I described above).
This confuses me in how to tackle the problem, because my objective is to recommend (personal recommendation) one of the two types of food so that the amount of sleep is maximized (and also constrained so that the amount of energy does not decline).
I tried modelling on the group assigned using the features described above, but that didn't help me because the group assigned in the first place is directly based on the amount of sleep. Do you have any suggestions?