As part of the UCI ICS Honors Program, I designed and implemented an end-to-end pipeline on the Wisconsin Sleep Cohort, starting from cleaning and reshaping 230+ clinical and polysomnography variables and converging on a compact, stable set of predictors. I iteratively refined the feature set, regularization strategy, and evaluation protocol whenever models overfit or produced confusing explanations, discussing edge cases with my advisor until we could surface sleep-related metrics as clear, defensible signals rather than opaque covariates. The final framework lets clinicians see how changes in sleep efficiency, non-REM duration, and onset latency shift cardiovascular risk estimates, and it motivated follow-up conversations about extending the approach to wearable-derived sleep data and broader, more diverse cohorts.