How to build a model where multiple data points contribute to a result I’m trying to figure out how to massage data and model the following scenario:

Customers at a restaurant rate the quality of the service between 1-10. 

I have data on individual interactions between the servers and customers. Say - length of interaction, type of interaction (refilling beverage, ordering, cleaning, etc). 

Hypothesis here is each interaction contributes to the final score. I want to build a model that tells me given an interaction, how does it move the score. 

My intuition is if I arranged the data as individual interactions, with output of final score, that’ll give me what I want. Is that true?
My intuition is if I arranged the data as individual interactions, with output of final score, that’ll give me what I want. Is that true?


If I understand correctly, your goal is to predict the final score that a customer gives at the end of their dinner based on all the interactions they had during the dinner, right? If yes then I don't think you can keep individual interactions as instances, because then (1) the model would only be able to use one interaction to predict the score and (2) the model would predict a different score for each interaction.

So each instance should represent the full meal and somehow contain as features all the possible information extracted from the interactions. For example an instance could contain the number of interactions, total length, number of refills, etc.

(side note: I wouldn't like eating in a restaurant where customers and staff are monitored so closely, but maybe that's just me)