Crowding prediction

Crowded conditions are common in environments such as shopping malls, airports, or hospital emergency departments, where numerous individuals seek service simultaneously. This project utilized time series data from a large hospital and applied machine learning models to predict crowding, overload, and churn in the emergency department. Predictions achieved over 90% accuracy, paving the way for future use in crowded institutions to optimize staffing more efficiently. This project was made as part of research at a marketing lab in the Hebrew University Business school.