Machine Learning-Based Prediction of Algerian University Student Participation in Sports Activities

Student participation in university sports is influenced by individual, social, cultural, and institutional factors. Despite the well-documented benefits of sports, barriers such as academic pressures and inadequate infrastructure hinder student involvement. This study employed a machine learning-based approach to predict sports participation among Algerian university students. Logistic regression and decision tree models were applied to analyze the dataset, focusing on key factors such as gender and athletic background. The models effectively predicted participation patterns, identifying gender and athletic background as significant determinants. Additionally, the analysis highlighted the most attractive sports disciplines for students, facilitating improved resource allocation and program design. The findings underscore the potential of machine learning to enhance university sports management. By providing actionable insights, this approach can guide the development of inclusive policies, fostering a dynamic and accessible sports ecosystem in Algerian universities.