This is a submission for the GitHub Finish-Up-A-Thon Challenge What I Built In 2018 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 Live demo: https://sanskriti1991.github.io/machineLearningProjects/finding_donors/ GitHub repo: https://github.com/sanskriti1991/machineLearningProjects/tree/master/finding_donors The demo lets you input census features and see how the model predicts donation likelihood.…