Costumer clustering in the insurance sector by means of unsupervised machine learning : an internship report

Detalhes bibliográficos
Autor(a) principal: Bucker, Thies
Data de Publicação: 2016
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/19789
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 Costumer clustering in the insurance sector by means of unsupervised machine learning : an internship reportCustomer segmentationClusteringK-meansUnsupervised learningSegmentationInternship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced AnalyticsClustering is one of the most frequently applied techniques in machine learning. An overview of the most comon algorithms, problems and solutions is provided in this report. In modern times, customer information is a curtail success factor in the insurance industry. This work describes a way how customer data can be leveraged in order to provide useful insights that help driving business in a more profitable way. It is shown that the available data can serve as a base for customer segmentation on which further models can be built upon. The customer is investigated in three dimensions (demographic, behavior, and value) that are crossed to gain precise information about customer segments.Castelli, MauroRUNBucker, Thies2017-01-16T14:43:03Z2016-10-252016-10-25T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/19789TID:201270994enginfo: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:RCAAP2024-03-11T04:01:59Zoai:run.unl.pt:10362/19789Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:25:41.840719Repositório 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 Costumer clustering in the insurance sector by means of unsupervised machine learning : an internship report
title Costumer clustering in the insurance sector by means of unsupervised machine learning : an internship report
spellingShingle Costumer clustering in the insurance sector by means of unsupervised machine learning : an internship report
Bucker, Thies
Customer segmentation
Clustering
K-means
Unsupervised learning
Segmentation
title_short Costumer clustering in the insurance sector by means of unsupervised machine learning : an internship report
title_full Costumer clustering in the insurance sector by means of unsupervised machine learning : an internship report
title_fullStr Costumer clustering in the insurance sector by means of unsupervised machine learning : an internship report
title_full_unstemmed Costumer clustering in the insurance sector by means of unsupervised machine learning : an internship report
title_sort Costumer clustering in the insurance sector by means of unsupervised machine learning : an internship report
author Bucker, Thies
author_facet Bucker, Thies
author_role author
dc.contributor.none.fl_str_mv Castelli, Mauro
RUN
dc.contributor.author.fl_str_mv Bucker, Thies
dc.subject.por.fl_str_mv Customer segmentation
Clustering
K-means
Unsupervised learning
Segmentation
topic Customer segmentation
Clustering
K-means
Unsupervised learning
Segmentation
description Internship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics
publishDate 2016
dc.date.none.fl_str_mv 2016-10-25
2016-10-25T00:00:00Z
2017-01-16T14:43:03Z
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/10362/19789
TID:201270994
url http://hdl.handle.net/10362/19789
identifier_str_mv TID:201270994
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
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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
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