Customer clustering in the health insurance industry by means of unsupervised machine learning

Detalhes bibliográficos
Autor(a) principal: Zaqueu, Jéssica Raquel
Data de Publicação: 2019
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/89468
Resumo: Internship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics
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spelling Customer clustering in the health insurance industry by means of unsupervised machine learningSegmentationClusteringCustomer AnalyticsCustomer ClusteringCustomer SegmentationInsuranceUnsupervised learningKMeansDecision TreesData MiningCustomer BehaviourHealth InsuranceInternship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced AnalyticsTo ensure competitiveness and relevancy in today’s highly digitised world, companies need to ensure that their focus is continuously on the client and on the experience they provide – while not having a negative effect on the organisation’s bottom line. A crucial step to achieving this is to get to know one’s customer base. With the vast amount of data available in a health insurance company, they are able to leverage on unsupervised machine learning techniques to segment their customers. This enables organisations to have a more tailored approach to their customers, identify market growth opportunities and gain competitive advantage.Vanneschi, LeonardoRufino, AndréEl-Jawhari, AnwarRUNZaqueu, Jéssica Raquel2019-12-06T18:34:53Z2019-11-192019-11-19T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/89468TID:202329437enginfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T04:39:49Zoai:run.unl.pt:10362/89468Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:37:00.637848Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Customer clustering in the health insurance industry by means of unsupervised machine learning
title Customer clustering in the health insurance industry by means of unsupervised machine learning
spellingShingle Customer clustering in the health insurance industry by means of unsupervised machine learning
Zaqueu, Jéssica Raquel
Segmentation
Clustering
Customer Analytics
Customer Clustering
Customer Segmentation
Insurance
Unsupervised learning
KMeans
Decision Trees
Data Mining
Customer Behaviour
Health Insurance
title_short Customer clustering in the health insurance industry by means of unsupervised machine learning
title_full Customer clustering in the health insurance industry by means of unsupervised machine learning
title_fullStr Customer clustering in the health insurance industry by means of unsupervised machine learning
title_full_unstemmed Customer clustering in the health insurance industry by means of unsupervised machine learning
title_sort Customer clustering in the health insurance industry by means of unsupervised machine learning
author Zaqueu, Jéssica Raquel
author_facet Zaqueu, Jéssica Raquel
author_role author
dc.contributor.none.fl_str_mv Vanneschi, Leonardo
Rufino, André
El-Jawhari, Anwar
RUN
dc.contributor.author.fl_str_mv Zaqueu, Jéssica Raquel
dc.subject.por.fl_str_mv Segmentation
Clustering
Customer Analytics
Customer Clustering
Customer Segmentation
Insurance
Unsupervised learning
KMeans
Decision Trees
Data Mining
Customer Behaviour
Health Insurance
topic Segmentation
Clustering
Customer Analytics
Customer Clustering
Customer Segmentation
Insurance
Unsupervised learning
KMeans
Decision Trees
Data Mining
Customer Behaviour
Health Insurance
description Internship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics
publishDate 2019
dc.date.none.fl_str_mv 2019-12-06T18:34:53Z
2019-11-19
2019-11-19T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/89468
TID:202329437
url http://hdl.handle.net/10362/89468
identifier_str_mv TID:202329437
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
repository.mail.fl_str_mv
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