Gyms Customer Loyalty Using the Logistic Regression Algorithm Technique

Descripción del Articulo

The objective of this work is to implement the logistic regression technique in datasets of gyms to identify and make a respective analysis for a correct segmentation of clients and, in this way, maximize the possibility of retaining clients; this will also allow us to rule out wrong decisions and i...

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Detalles Bibliográficos
Autores: Munoz, Joaquin Moreno, Osorio, Miguel Ramirez, Rodriguez, Ciro, Rodriguez, Diego, Lezama, Pedro, Pomachagua, Yuri
Formato: artículo
Fecha de Publicación:2022
Institución:Universidad Peruana de Ciencias Aplicadas
Repositorio:UPC-Institucional
Lenguaje:inglés
OAI Identifier:oai:repositorioacademico.upc.edu.pe:10757/669235
Enlace del recurso:http://hdl.handle.net/10757/669235
Nivel de acceso:acceso embargado
Materia:algorithm
customer loyalty
gyms
logistic regression
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dc.title.es_PE.fl_str_mv Gyms Customer Loyalty Using the Logistic Regression Algorithm Technique
title Gyms Customer Loyalty Using the Logistic Regression Algorithm Technique
spellingShingle Gyms Customer Loyalty Using the Logistic Regression Algorithm Technique
Munoz, Joaquin Moreno
algorithm
customer loyalty
gyms
logistic regression
title_short Gyms Customer Loyalty Using the Logistic Regression Algorithm Technique
title_full Gyms Customer Loyalty Using the Logistic Regression Algorithm Technique
title_fullStr Gyms Customer Loyalty Using the Logistic Regression Algorithm Technique
title_full_unstemmed Gyms Customer Loyalty Using the Logistic Regression Algorithm Technique
title_sort Gyms Customer Loyalty Using the Logistic Regression Algorithm Technique
author Munoz, Joaquin Moreno
author_facet Munoz, Joaquin Moreno
Osorio, Miguel Ramirez
Rodriguez, Ciro
Rodriguez, Diego
Lezama, Pedro
Pomachagua, Yuri
author_role author
author2 Osorio, Miguel Ramirez
Rodriguez, Ciro
Rodriguez, Diego
Lezama, Pedro
Pomachagua, Yuri
author2_role author
author
author
author
author
dc.contributor.author.fl_str_mv Munoz, Joaquin Moreno
Osorio, Miguel Ramirez
Rodriguez, Ciro
Rodriguez, Diego
Lezama, Pedro
Pomachagua, Yuri
dc.subject.es_PE.fl_str_mv algorithm
customer loyalty
gyms
logistic regression
topic algorithm
customer loyalty
gyms
logistic regression
description The objective of this work is to implement the logistic regression technique in datasets of gyms to identify and make a respective analysis for a correct segmentation of clients and, in this way, maximize the possibility of retaining clients; this will also allow us to rule out wrong decisions and incorrect assumptions, in addition to optimizing management according to customer data, which will be vital for a correct loyalty of gym users. The precision calculated the loyalty by the logistic regression algorithm considering important factors such as the rate of abandonment.
publishDate 2022
dc.date.accessioned.none.fl_str_mv 2023-11-07T16:36:42Z
dc.date.available.none.fl_str_mv 2023-11-07T16:36:42Z
dc.date.issued.fl_str_mv 2022-01-01
dc.type.es_PE.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.doi.none.fl_str_mv 10.1109/CICN56167.2022.10008370
dc.identifier.uri.none.fl_str_mv http://hdl.handle.net/10757/669235
dc.identifier.journal.es_PE.fl_str_mv Proceedings - 2022 14th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2022
dc.identifier.eid.none.fl_str_mv 2-s2.0-85146851691
dc.identifier.scopusid.none.fl_str_mv SCOPUS_ID:85146851691
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identifier_str_mv 10.1109/CICN56167.2022.10008370
Proceedings - 2022 14th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2022
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dc.language.iso.es_PE.fl_str_mv eng
language eng
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dc.publisher.es_PE.fl_str_mv Institute of Electrical and Electronics Engineers Inc.
dc.source.es_PE.fl_str_mv Universidad Peruana de Ciencias Aplicadas (UPC)
Repositorio Academico - UPC
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dc.source.journaltitle.none.fl_str_mv Proceedings - 2022 14th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2022
dc.source.beginpage.none.fl_str_mv 204
dc.source.endpage.none.fl_str_mv 212
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spelling 9a1cb9258a3aefb3972d5c1b434344b43009f2c11ccda525107a765d8768383741a3001481cf04c578bc015c4403f7826ae2f25008001a598412e418a24a4afd84d68f628062a7deb0d67c2c464054d24853cd721040bf31ff65f6806d23fcaf71e9ea5d3300Munoz, Joaquin MorenoOsorio, Miguel RamirezRodriguez, CiroRodriguez, DiegoLezama, PedroPomachagua, Yuri2023-11-07T16:36:42Z2023-11-07T16:36:42Z2022-01-0110.1109/CICN56167.2022.10008370http://hdl.handle.net/10757/669235Proceedings - 2022 14th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 20222-s2.0-85146851691SCOPUS_ID:851468516910000 0001 2196 144X047xrr705The objective of this work is to implement the logistic regression technique in datasets of gyms to identify and make a respective analysis for a correct segmentation of clients and, in this way, maximize the possibility of retaining clients; this will also allow us to rule out wrong decisions and incorrect assumptions, in addition to optimizing management according to customer data, which will be vital for a correct loyalty of gym users. The precision calculated the loyalty by the logistic regression algorithm considering important factors such as the rate of abandonment.Revisión por paresapplication/pdfengInstitute of Electrical and Electronics Engineers Inc.info:eu-repo/semantics/embargoedAccessAttribution-NonCommercial-ShareAlike 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-sa/4.0/Universidad Peruana de Ciencias Aplicadas (UPC)Repositorio Academico - UPCProceedings - 2022 14th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2022204212reponame:UPC-Institucionalinstname:Universidad Peruana de Ciencias Aplicadasinstacron:UPCalgorithmcustomer loyaltygymslogistic regressionGyms Customer Loyalty Using the Logistic Regression Algorithm Techniqueinfo:eu-repo/semantics/articleLICENSElicense.txtlicense.txttext/plain; charset=utf-81748https://repositorioacademico.upc.edu.pe/bitstream/10757/669235/2/license.txt8a4605be74aa9ea9d79846c1fba20a33MD52falseCC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-81031https://repositorioacademico.upc.edu.pe/bitstream/10757/669235/1/license_rdf934f4ca17e109e0a05eaeaba504d7ce4MD51false10757/669235oai:repositorioacademico.upc.edu.pe:10757/6692352023-11-07 16:36:43.064Repositorio académico upcupc@openrepository.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Gyms Customer Loyalty Using the Logistic Regression Algorithm Technique
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