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I would really appreciate some help on the first steps to my problem, suggestions of modeling techniques i could use or relevant research (i could not find any).

I have a list of ingredients (150 in total) and a list of recipes (100 or so) with data on which combination of ingredients are present in each recipe (on average 4-5 ingredients per recipe).

My output variables are 4 metrics of ratings of those recipes (taste, smell, look, and texture) for each of those 100 recipes from a scale of 1-10 for each.

My aim is to predict the 4 metrics based on a new combination of ingredients (picked from the list) that i specify.

What modeling technique should I look at to help solve this prediction problem?

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Here are a few ideas to start with:

A simple model would be to use multiple linear regression (MLR), or a Random Forest

If you want to evaluate only based on if an ingredient is used, your input dataset could look like this:

Butter Flour Eggs ... Test Smell Look Texture 1 0 1 1 5 2 4

If you want to predict on how much of each ingredient (some is better, too much is not good) your dataset could look like this:

Butter Flour Eggs ... Test Smell Look Texture 1.5 3.0 2 1 5 2 4

You can get some other ideas from this website:

https://machinelearningmastery.com/multi-output-regression-models-with-python/

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