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I am working on a machine learning project that I want to rank each customer and put on a scale smt like one of those

https://cdn1.vectorstock.com/i/1000x1000/90/40/credit-score-indicators-with-color-levels-from-vector-18919040.jpg

I've got almost 20k customer loan proposal data (customer information (~100 feature) + result of the proposal), each proposal end up with a decision (approve, reject, conditional approve) but no "customer rating" is labeled during the evaluation process so I don't know "goodness" of a customer

I thought about clustering customers but I can't be sure about number of clusters and I would really need of a expert who will put each cluster into related colors. Even after, I have no clue how to differentiate 2 customers which fall into the same color zone (green vs super green)

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    $\begingroup$ You need to define "goodness". Is it probability of repaying the loan from 0 (0% chance) to 1 (100% likely)? I get the impression that's basically what a credit score is, but on a different numeric scale. Once you have that definition agreed upon, you would use historical data on that customer to compute the % of loans (or amount of money?) they repaid, and then that could be the target for a regression problem. $\endgroup$ – CalZ Sep 23 at 13:51

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