Customer clustering in the health insurance industry by means of unsupervised machine learning
Autor(a) principal: | |
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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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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 |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
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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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1799137987567550464 |