Predictive Forecasting of Care Load & Placement Demand: What a 66% Structural Break Taught Me About Machine Learning

HHS Unaccompanied Alien Children Program — Data Science Internship Project Sugnik Mondal · Unified Mentor Data Science Intern · March 2026 TL;DR: I built a forecasting system for the HHS UAC Program using 720 real records. Nine models were tested. Eight failed or underperformed. One won — but not because of model complexity. The reason every sophisticated model failed, and what fixed it, is the actual story here. Abstract The U.S. Department of Health & Human Services (HHS) Unaccompanied Alien Children (UAC) Program manages the care, custody, and sponsor placement of migrant children arriving at the U.S. border. Daily care load fluctuated between 1,972 and 11,516 children during the study period — a 5.8× range that makes capacity planning extremely difficult without reliable forecasts. This paper presents a complete ML forecasting system built on 720 real operational records spanning January 2023 to December 2025. The central finding is that a January 2025 structural break — a permanen