International Journal of Scientific Research and Engineering Development

This project uses simulation and machine learning to predict workout completion in gyms, offering valuable insights into how waiting times, fitness levels, and visitor behavior affect exercise outcomes. By simulating 200 gym visitors with diverse demographics and workout habits, the study reveals that longer waiting times and lower fitness levels significantly impact the likelihood of completing workouts. The Random Forest classifier achieved 95% accuracy and 100% recall, proving its effectiveness in identifying visitors who will finish their workouts. This approach helps optimize gym resource allocation, reduce waiting times, and improve operational decisions. Ultimately, the project enhances visitor satisfaction by offering actionable strategies for improving gym experiences, better managing equipment and trainer availability, and boosting overall gym efficiency.