Customer churn prediction in the banking industry

Detalhes bibliográficos
Autor(a) principal: Cunha, Soraia Sofia Santiago da
Data de Publicação: 2023
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/149182
Resumo: Internship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Business Analytics
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spelling Customer churn prediction in the banking industryCustomer churn predictionBankingMachine learningSupervised learningInternship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Business AnalyticsThe objective of this project is to create a predictive model that will decrease customer churn in a Portuguese bank. That is, we intend to identify customers who could be considering closing their checking accounts. For the bank to be able to take the necessary corrective measures, the model also aims to determine the characteristics of the customers that decided to leave. This model will make use of customer data that the organization already has to hand. Data pre-processing with data cleansing, transformation, and reduction was the initial stage of the analysis. The dataset is imbalanced, meaning that we have a small number of positive outcomes or churners; thus, under-sampling and other approaches were employed to address this issue. The predictive models used are logistic regression, support vector machine, decision trees and artificial neural networks, and for each, parameter tuning was also conducted. In conclusion, regarding the customer churn prediction, the recommended model is a support vector machine with a precision of 0.84 and an AUROC of 0.905. These findings will contribute to the customer lifetime value, helping the bank better understand their customers' behavior and allow them to draw strategies accordingly with the information obtained.Jesus, Frederico Miguel Campos Cruz Ribeiro deRUNCunha, Soraia Sofia Santiago da2023-02-14T18:27:23Z2023-01-252023-01-25T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/149182TID:203227581enginfo: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:RCAAP2024-03-11T05:30:59Zoai:run.unl.pt:10362/149182Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:53:39.168583Repositório 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 the banking industry
title Customer churn prediction in the banking industry
spellingShingle Customer churn prediction in the banking industry
Cunha, Soraia Sofia Santiago da
Customer churn prediction
Banking
Machine learning
Supervised learning
title_short Customer churn prediction in the banking industry
title_full Customer churn prediction in the banking industry
title_fullStr Customer churn prediction in the banking industry
title_full_unstemmed Customer churn prediction in the banking industry
title_sort Customer churn prediction in the banking industry
author Cunha, Soraia Sofia Santiago da
author_facet Cunha, Soraia Sofia Santiago da
author_role author
dc.contributor.none.fl_str_mv Jesus, Frederico Miguel Campos Cruz Ribeiro de
RUN
dc.contributor.author.fl_str_mv Cunha, Soraia Sofia Santiago da
dc.subject.por.fl_str_mv Customer churn prediction
Banking
Machine learning
Supervised learning
topic Customer churn prediction
Banking
Machine learning
Supervised learning
description Internship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Business Analytics
publishDate 2023
dc.date.none.fl_str_mv 2023-02-14T18:27:23Z
2023-01-25
2023-01-25T00:00:00Z
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/10362/149182
TID:203227581
url http://hdl.handle.net/10362/149182
identifier_str_mv TID:203227581
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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