Machine Learning - Based Prediction of Algerian University Student Participation in Sports Activities
DOI:
https://doi.org/10.24193/subbeag.69(4).32Keywords:
Sport, University, Prediction, Activity, AlgeriaAbstract
Student participation in university sports is influenced by individual, social, cultural, and institutional factors. Despite the recognized benefits of sports, many students face barriers such as academic pressures and inadequate infrastructure. This study proposed a machine learning-based approach to predict sports participation among Algerian university students, focusing on identifying key factors like gender and athletic background to guide inclusive sports policies. Using models like logistic regression and decision trees, the study effectively predicted participation patterns and highlighted the most attractive sports disciplines, enabling better resource planning and tailored programs. This approach offers valuable insights for fostering a dynamic, inclusive sports ecosystem and emphasizes the potential of machine learning to enhance university sports management.
Article history: Received: 2024 November 07; Revised 2025 January 20; Accepted 2025 January 22; Available online: 2025 February 10; Available print: 2025 February 28
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