A Data-Driven Approach to Predicting Recreational Activity Participation Using Machine Learning

• Published in: Research quarterly for exercise and sport Vol. 95; no. 4; pp. 873 - 885
• Main Authors: Lee, Seungbak, Kang, Minsoo
• Format: Journal Article
• Language: English
• Published: United States Routledge 01.10.2024
• Subjects: Adult; Algorithms; CRISP-DM; Decision Trees; Educational Status; Exercise; Female; Humans; Life Style; Logistic Models; Machine Learning; Male; Middle Aged; Nutrition Surveys; permutation feature importance; prediction model; Recreation; Support Vector Machine; CRISP-DM; Algorithms; prediction model; permutation feature importance

Purpose: With the popularity of recreational activities, the study aimed to develop prediction models for recreational activity participation and explore the key factors affecting participation in recreational activities. Methods: A total of 12,712 participants, excluding individuals under 20, were...