Operational teams need a clearer view of care-load pressure, backlog trends, and near-term capacity risk across changing system conditions.
Built a Streamlit analytics system with KPI monitoring, pressure anomaly views, a Random Forest 7-day prediction model, and scenario simulation.
The dashboard connects current load, forecasted arrivals, and model insight views so capacity pressure is easier to monitor and explain.
Workflow
01Load signals
02Backlog flow
03Pressure anomaly
047-day model
05Scenario view
Role and technical focus
Forecasting model, KPI design, scenario simulator, dashboard build
PythonRandom ForestStreamlitPandasAnalyticsForecasting
Notes
- Random Forest Regressor used for 7-day arrival prediction.
- Dashboard includes pressure, volatility, backlog, and scenario simulation views.
- Metrics are reported as model performance, not causal claims.
Project media