Shared Data, Distinct Models: Exploring Diverse ML Approaches in a Team Science Framework

This presentation highlights a mentor-led, student-driven collaboration to understand predictors of PrEP knowledge using diverse machine learning approaches. Working from a shared public health dataset, each team member applied a distinct modeling strategy—including logistic regression, elastic net, random forests, and deep learning neural networks—to explore the research question from different analytical angles. The talk will describe how these parallel analyses offered complementary perspectives on the research question and reflect on how team-based exploration of modeling strategies supports skill development and applied learning in public health data science.