A data mining approach to predict probabilities of football matches

Detalhes bibliográficos
Autor(a) principal: Tiago Filipe Mendes Neves
Data de Publicação: 2019
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: https://hdl.handle.net/10216/121217
Resumo: With the increasing growth of the amount of money invested in sports betting markets it is important to verify how far the machine learning techniques can bring value to this area. A performance evaluation of the state-of-art algorithms is performed and evaluated according to several metrics, incorporated in the CRISP-DM methodology that goes from web-scraping through to generation and selection of features. It is also explored the universe of ensemble techniques in an attempt to improve the models from the point of view of bias-variance trade-off, with a special focus on neural network ensembles.
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spelling A data mining approach to predict probabilities of football matchesEngenharia electrotécnica, electrónica e informáticaElectrical engineering, Electronic engineering, Information engineeringWith the increasing growth of the amount of money invested in sports betting markets it is important to verify how far the machine learning techniques can bring value to this area. A performance evaluation of the state-of-art algorithms is performed and evaluated according to several metrics, incorporated in the CRISP-DM methodology that goes from web-scraping through to generation and selection of features. It is also explored the universe of ensemble techniques in an attempt to improve the models from the point of view of bias-variance trade-off, with a special focus on neural network ensembles.2019-07-112019-07-11T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttps://hdl.handle.net/10216/121217TID:202398200engTiago Filipe Mendes Nevesinfo: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:RCAAP2023-11-29T14:33:47Zoai:repositorio-aberto.up.pt:10216/121217Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T00:03:58.038914Repositório 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 A data mining approach to predict probabilities of football matches
title A data mining approach to predict probabilities of football matches
spellingShingle A data mining approach to predict probabilities of football matches
Tiago Filipe Mendes Neves
Engenharia electrotécnica, electrónica e informática
Electrical engineering, Electronic engineering, Information engineering
title_short A data mining approach to predict probabilities of football matches
title_full A data mining approach to predict probabilities of football matches
title_fullStr A data mining approach to predict probabilities of football matches
title_full_unstemmed A data mining approach to predict probabilities of football matches
title_sort A data mining approach to predict probabilities of football matches
author Tiago Filipe Mendes Neves
author_facet Tiago Filipe Mendes Neves
author_role author
dc.contributor.author.fl_str_mv Tiago Filipe Mendes Neves
dc.subject.por.fl_str_mv Engenharia electrotécnica, electrónica e informática
Electrical engineering, Electronic engineering, Information engineering
topic Engenharia electrotécnica, electrónica e informática
Electrical engineering, Electronic engineering, Information engineering
description With the increasing growth of the amount of money invested in sports betting markets it is important to verify how far the machine learning techniques can bring value to this area. A performance evaluation of the state-of-art algorithms is performed and evaluated according to several metrics, incorporated in the CRISP-DM methodology that goes from web-scraping through to generation and selection of features. It is also explored the universe of ensemble techniques in an attempt to improve the models from the point of view of bias-variance trade-off, with a special focus on neural network ensembles.
publishDate 2019
dc.date.none.fl_str_mv 2019-07-11
2019-07-11T00:00:00Z
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 https://hdl.handle.net/10216/121217
TID:202398200
url https://hdl.handle.net/10216/121217
identifier_str_mv TID:202398200
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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