The Number Your Accuracy Score Is Not Telling You: What I Learned Auditing My Student ML Project

This is a submission for the GitHub Finish-Up-A-Thon Challenge

What I Built

In 2017 I trained a charity donor classifier for a Udacity machine learning nanodegree project. The task was to predict whether someone earns more than $50k per year as a proxy for donation likelihood for a fictional charity called CharityML. Gradient Boosting won the model comparison at 86.78% accuracy and an F-score of 0.7469. I submitted it, got my grade, and filed the notebook away.

Coming back in 2026, I did not just fix the code. I audited what the model actually learned. The answer was uncomfortable.

Demo