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

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 p...

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Autores principales:	Qiuying Chen, SangJoon Lee
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	home workout platform machine learning prediction customer usage Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/b8a9511e30004ffcbfdd0e4fad15f8ae
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spelling	oai:doaj.org-article:b8a9511e30004ffcbfdd0e4fad15f8ae2021-11-11T15:01:24ZA Machine Learning Approach to Predict Customer Usage of a Home Workout Platform10.3390/app112199272076-3417https://doaj.org/article/b8a9511e30004ffcbfdd0e4fad15f8ae2021-10-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/21/9927https://doaj.org/toc/2076-3417Health 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.Qiuying ChenSangJoon LeeMDPI AGarticlehome workoutplatformmachine learningpredictioncustomer usageTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 9927, p 9927 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	home workout platform machine learning prediction customer usage Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	home workout platform machine learning prediction customer usage Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Qiuying Chen SangJoon Lee A Machine Learning Approach to Predict Customer Usage of a Home Workout Platform
description	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.
format	article
author	Qiuying Chen SangJoon Lee
author_facet	Qiuying Chen SangJoon Lee
author_sort	Qiuying Chen
title	A Machine Learning Approach to Predict Customer Usage of a Home Workout Platform
title_short	A Machine Learning Approach to Predict Customer Usage of a Home Workout Platform
title_full	A Machine Learning Approach to Predict Customer Usage of a Home Workout Platform
title_fullStr	A Machine Learning Approach to Predict Customer Usage of a Home Workout Platform
title_full_unstemmed	A Machine Learning Approach to Predict Customer Usage of a Home Workout Platform
title_sort	machine learning approach to predict customer usage of a home workout platform
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/b8a9511e30004ffcbfdd0e4fad15f8ae
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A Machine Learning Approach to Predict Customer Usage of a Home Workout Platform

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