Customer Churn Prediction in Portuguese Banking Sector: Using a Machine Learning Approach

Detalhes bibliográficos
Autor(a) principal: Pires, Inês Tomás
Data de Publicação: 2024
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/163771
Resumo: Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business Intelligence
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spelling Customer Churn Prediction in Portuguese Banking Sector: Using a Machine Learning ApproachBanking SectorBusiness IntelligenceCustomer ChurnMachine LearningPower BISDG 8 - Decent work and economic growthSDG 9 - Industry, innovation and infrastructureDomínio/Área Científica::Ciências Naturais::Ciências da Computação e da InformaçãoDissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceThis study focuses on developing a predictive model for customer churn in a Portuguese bank, using machine learning techniques. Following the CRISP-DM methodology, the analysis encompasses comprehensive EDA, data preparation and visualizations, laying the foundation for model selection. Whitin the subset of evaluated models, such as tree-based and ensembled models, Gradient Boosting emerges as a standout performer, demonstrating notable predictive capabilities. Beyond the identification of customers at risk to churn, this model provides valuable insights, crafting proactive retention strategies. The precision in identifying customers with a high probability of churn enhances informed decision-making. For that reason, an interactive dashboard is developed to empower stakeholders in addressing potential churn risks. These findings underscore the importance of leveraging machine learning in banking scenarios, emphasizing the potential for predictive analytics to enhance customer retention strategies and overall business outcomes.Neto, Miguel de Castro Simões FerreiraJardim, João Bruno Morais de SousaRUNPires, Inês Tomás2024-02-19T19:14:02Z2024-01-292024-01-29T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/163771TID:203524896enginfo: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:RCAAP2024-03-11T05:48:41Zoai:run.unl.pt:10362/163771Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:59:51.035751Repositório 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 Churn Prediction in Portuguese Banking Sector: Using a Machine Learning Approach
title Customer Churn Prediction in Portuguese Banking Sector: Using a Machine Learning Approach
spellingShingle Customer Churn Prediction in Portuguese Banking Sector: Using a Machine Learning Approach
Pires, Inês Tomás
Banking Sector
Business Intelligence
Customer Churn
Machine Learning
Power BI
SDG 8 - Decent work and economic growth
SDG 9 - Industry, innovation and infrastructure
Domínio/Área Científica::Ciências Naturais::Ciências da Computação e da Informação
title_short Customer Churn Prediction in Portuguese Banking Sector: Using a Machine Learning Approach
title_full Customer Churn Prediction in Portuguese Banking Sector: Using a Machine Learning Approach
title_fullStr Customer Churn Prediction in Portuguese Banking Sector: Using a Machine Learning Approach
title_full_unstemmed Customer Churn Prediction in Portuguese Banking Sector: Using a Machine Learning Approach
title_sort Customer Churn Prediction in Portuguese Banking Sector: Using a Machine Learning Approach
author Pires, Inês Tomás
author_facet Pires, Inês Tomás
author_role author
dc.contributor.none.fl_str_mv Neto, Miguel de Castro Simões Ferreira
Jardim, João Bruno Morais de Sousa
RUN
dc.contributor.author.fl_str_mv Pires, Inês Tomás
dc.subject.por.fl_str_mv Banking Sector
Business Intelligence
Customer Churn
Machine Learning
Power BI
SDG 8 - Decent work and economic growth
SDG 9 - Industry, innovation and infrastructure
Domínio/Área Científica::Ciências Naturais::Ciências da Computação e da Informação
topic Banking Sector
Business Intelligence
Customer Churn
Machine Learning
Power BI
SDG 8 - Decent work and economic growth
SDG 9 - Industry, innovation and infrastructure
Domínio/Área Científica::Ciências Naturais::Ciências da Computação e da Informação
description Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business Intelligence
publishDate 2024
dc.date.none.fl_str_mv 2024-02-19T19:14:02Z
2024-01-29
2024-01-29T00:00:00Z
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/10362/163771
TID:203524896
url http://hdl.handle.net/10362/163771
identifier_str_mv TID:203524896
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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