FICO score Default Estimator
- 5 Devlogs
- 3 Total hours
A FICO score quantization engine that maps continuous credit scores (300–850) into optimal buckets using dynamic programming to maximize log-likelihood of mortgage default prediction.
I finally finished the README and finished the print statements for the FICO scores distributions. Additionally, I found the default probability and the log- likelihood score.
Im finally done with the actual coding part itself, it has taken such a long time trying to debug and figure out the formulas. I’m about to get started on the README now.
I have run into a bug, where even if I fix the python syntax it still doesn’t run properly. I am trying everything in my arsenal to get this to work.
I figured out how to get the FICO scores sorted and am currently working on implementing the log functions into my code.
My goal is FICO score quantization, which means converting continuous FICO scores (300–850) into discrete buckets/ratings for a machine learning model that predicts mortgage default probability.
