Portfolio Project

COVID-19 Outbreak Drivers

Python XGBoost & SHAP

Analytics Tourism Python AWS

Demo

STAR Summary

Situation
Using 2020-2023 HHS hospital-capacity data, I built an early-warning model to flag states at risk of crossing 90% ICU utilization in the next 7 days.
Task
Owned the end-to-end build, from implementation through the final deliverable.
Action
  • Cleaned and enriched 50k+ rows from the HHS hospital-capacity time series; added rolling stats, trends, and 1/3/7/14-day lag features.
  • Trained an XGBoost classifier with class-imbalance weighting and a strict time-based train/test split.
Result
  • Used SHAP to highlight the top drivers and embedded an interactive plot in the report.
  • Top driver was the share of ICU beds occupied by COVID patients.
  • Exported daily, per-state risk scores (probability of breaching 90% ICU utilization within 7 days).

Notes

The model is a 7-day state-level ICU breach risk scorer based on HHS hospital-capacity data, not a clinical forecast.