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Applied ML · Data · 2025
Chemistry Outcome Modeling
A model built for Emory's Chemistry Unbound program to flag students who might struggle on the DUCK exam early, using placement scores, GPA features, course grades, and standardized-test data.
Problem
Support programs need a signal early, well before the final exam results come in.
Approach
I pulled placement exams, GPA features, course grades, and a few model families into one prediction workflow.
Outcome
An early-warning workflow that turns past records into a heads-up for who to reach out to, trained on 223 students and scored across 2,866 downstream.