A Machine Learning Approach to Predict Customer Usage of a Home Workout Platform

Abstract

Health authorities have recommended the use of digital tools for home workouts to stay active and healthy during the COVID-19 pandemic. In this paper, a machine learning approach is proposed to assess the activity of users on a home workout platform. Keep is a home workout application dedicated to providing one-stop exercise solutions such as fitness teaching, cycling, running, yoga, and fitness diet guidance. We used a data crawler to collect the total training set data of 7734 Keep users and compared four supervised learning algorithms: support vector machine, k-nearest neighbor, random forest, and logistic regression. The receiver operating curve analysis indicated that the overall discrimination verification power of random forest was better than that of the other three models. The random forest model was used to classify 850 test samples, and a correct rate of 88% was obtained. This approach can predict the continuous usage of users after installing the home workout application. We considered 18 variables on Keep that were expected to affect the determination of continuous participation. Keep certification is the most important variable that affected the results of this study. Keep certification refers to someone who has verified their identity information and can, therefore, obtain the Keep certification logo. The results show that the platform still needs to be improved in terms of real identity privacy information and other aspects.

Keywords

• home workout
• platform
• machine learning
• prediction
• customer usage