A Comparative Study of Machine Learning Algorithms and Explainable AI for Fitness Club Attendance Classification

The growing interest in health and fitness has resulted in an increase in the number of people joining fitness clubs. However, maintaining member engagement remains a challenge for fitness club operators. The goal of this study is to create a binary classification model that predicts whether a member is likely to attend a fitness club or not. In this paper, we propose a framework to explain how good the fitness club attendance classification performance will be under various Machine learning models by understanding its metrics. In this study by employing several classification models to determine Fitness Club attendance Classification using machine learning algorithms such as Random Forest, Gradient Boosting, Light GBM, etc. The Random Forest classifier performs with a higher accuracy of 0.87, and Explainable AI has been used to reveal the most significant feature.