Hospital Data Forecasting

Hospital Data Forecasting is a predictive analysis project for the emergency department activity of Pitié-Salpêtrière Hospital (Paris). The system uses a SARIMA(1,1,1)(1,1,1,12) model trained on 132 monthly observations (2012-2022) to forecast activity over 24 months (2023-2024) with 95% confidence intervals. The project includes an in-depth COVID-19 impact analysis, crisis scenario simulation, and an interactive Streamlit dashboard with temporal filtering and crisis intensity adjustment. Team project of 4 for Epitech Digital School.

Challenges

• Building the dataset from PSL-CFX annual reports
• Modeling seasonality and the exceptional COVID-19 impact
• Creating reliable forecasts with confidence intervals for hospital planning
• Simulating crisis scenarios with adjustable intensity

Solutions

• Data pipeline with extraction and cleaning from annual reports
• SARIMA(1,1,1)(1,1,1,12) model optimized for monthly seasonality
• Interactive Streamlit dashboard with Normal and Crisis modes
• 20+ visualizations (Plotly, Seaborn) with technical report and presentation

Results

• R² = 89%, MAE = 892 visits, MAPE = 6.2%
• 24-month forecasts with 95% confidence intervals
• Interactive dashboard with temporal filtering and crisis simulation
• Complete deliverables: dashboard, technical report, implementation plan

Technologies

Python · SARIMA · Streamlit · Pandas · Plotly · Scikit-learn · Statsmodels