# A Machine Learning Approach to Predict Customer Usage of a Home Workout Platform
## 1 Introduction  
Digital technologies have profoundly and intensively changed social life, including transforming people's sports activities and health behaviors. Home workout clubs and influencers make it possible for followers and members to practice fitness activities in almost any place and time. In addition, online platforms demonstrate lifestyle sports and fitness tendencies and popularize messages associated with health and fitness [1]. Several accounts elaborately spread the experiences in applying fitness and physical activity applications or wearable devices. Others evaluate technical contributions to the level of such physical activities or investigate gender and age difference in the adoption of fitness applications [2]. To mitigate the transmission of the coronavirus disease (COVID-19) and reduce the interactions between unrecognized infected individuals and non-infected individuals, many measures have been applied, including quarantine, local confinement, lockdown, and isolation. Therefore, people have no access to places such as fitness studios, sports clubs, swimming pools, and other public facilities in this special period of time. In addition to the sedentary habits people may have, the reduction of outdoor daily activities and restriction to the indoor environment at home leads to a reduction in the level of physical activity. In this regard, the World Health Organization and health authorities have recommended digital tools for home-based sport and exercise routines to maintain a fundamental level of physical activity during the pandemic [3,4]. As indicated by a global study, professionals from the health and fitness industry consider \"online fitness\" as the leading fitness tendency in 2021 [5]. Influenced by COVID19, home workout has become popular as it enables individuals to easily practice activities at home without being limited by space and time. However, studies have indicated that the adoption of digital sports activities during the pandemic remains insufficient. Therefore, studies have been conducted to determine the factors that affect the practice of home workouts using big data analysis. Machine learning is a sub-discipline of artificial intelligence (AI) in which computers derive new information or decisions by learning data using algorithms and programs, similar to human learning [6]. To obtain good discriminant results, an appropriate selection of variables is necessary, but the importance of certain variables remains unclear. In general, to exercise at home, an individual needs a timer or watch to time the activity or an application such as Keep. Keep is a sports application commonly used in China that has recently announced that it reached 100 million registered users, becoming the first Chinese sports application to reach this mark and China's largest social platform for sports [7]. This application integrates big data and AI with a focus on sports and employs science and technology to improve users' sports experiences and needs. Thus, this study aimed to find effective methods to determine whether users participated in an exercise in a given month, focusing on the home workout application Keep and using big data to propose practical solutions with a new approach. Advanced analysis based on machine learning has become increasingly popular because it can provide business advantages for almost every industry. 'Shelter in place' and 'lockdown' orders implemented to minimize the spread of COVID-19 reduced opportunities to be physically active. For many, the home environment was the only viable option to practice physical exercises. Although COVID-19 has accelerated the development of the home workout industry, there are few studies evaluating the use of machine learning in this field. Therefore, we adopted a machine learning approach to predict whether home workouts will continue to be used based on the following reasons.