This project forecasts monthly healthcare call volumes using time-series modeling (ARIMA), optimizing call center staffing and reducing wait times. Features include trend analysis, model tuning, and an interactive dashboard for real-time insights.

Simulates incident handling in data centers using Python and SimPy. Analyze how staffing levels, shift timing, and triage rules affect SLA compliance, resolution time, and backlog size.

An analysis of nurse staffing patterns, focusing on ownership type, staffing levels, and facility ratings, to optimize strategies and improve patient care and business outcomes.