• Shahad Mohammed Al-Haj
• Rana Abdullah Al-Masabi
• Reem Saleh Al-Masabi
• Ahmed Al-Masabi

This study aimed to predict gym members' attendance over the next month using multiple machine learning models. Four models were implemented: Random Forest, Gradient Boosting, Support Vector Machine (SVM), and Gaussian Naive Bayes. The results showed that Random Forest, Gradient Boosting, and SVM performed exceptionally well (Accuracy = 1.00, Precision = 1.00, Recall = 1.00, F1 = 1.00), while Gaussian Naive Bayes performed worse (Accuracy = 0.8519). The significance of the features and analytics indicates that weekly attendance behavior—frequency of visits, number of days attended, and workout duration—is the strongest predictor of attendance. These results provide a solid foundation for implementing member targeting and retention policies

Machine Learning, Gym Attendance, Retention Prediction, Random Forest, SVM, Behavioral Analytics.

"Predicting Gym Member Attendance Using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 12, page no.a652-a655, December-2025, Available :http://www.jetir.org/papers/JETIR2512087.pdf

"Predicting Gym Member Attendance Using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 12, page no. ppa652-a655, December-2025, Available at : http://www.jetir.org/papers/JETIR2512087.pdf