Predictive modeling of motor client's propensity to churn

Detalhes bibliográficos
Autor(a) principal: Lashkajani, Masoud Mirzakazemi
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/10400.5/29475
Resumo: Mestrado Bolonha em Data Analytics for Business
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spelling Predictive modeling of motor client's propensity to churnSupervised LearningClassificationCustomer Churn PredictionPredictive modelK-Neighbors ClassifierDecision TreeLogistic RegressionRandom ForestExtreme Gradient BoostingLightGBMMestrado Bolonha em Data Analytics for BusinessThis master's thesis focuses on the predictive modeling of motor client's propensity to churn at Lusitania Seguros company in Portugal, Utilizing a dataset of 248,218 observations with 45 distinct features from 2021 to 2022, the study highlights the significance of understanding customer churn in the competitive insurance industry. The research methodology involves applying various machine learning models including Logistic Regression, Decision Tree, Random Forest, XGBoost, K-Nearest Neighbors, and LightGBM. A significant part of the study was the use of the Synthetic Minority Over-sampling Technique (SMOTE) to address class imbalance, coupled with rigorous validation methods like stratified K-fold cross-validation and a hold-out test set. The findings indicate a close performance between the LightGBM and XGBoost models. LightGBM slightly outperforms with an accuracy of 0.98, demonstrating high recall, precision, and F1-score for the minority class. XGBoost closely matches these metrics, presenting itself as an equally effective alternative for churn prediction. The thesis also delves into feature importance analysis from the LightGBM model, providing valuable insights for targeted customer retention strategies at Lusitania Seguros.Instituto Superior de Economia e GestãoBastos, JoãoFranco, LuísSilva, Paulo MartinsRepositório da Universidade de LisboaLashkajani, Masoud Mirzakazemi2023-032023-03-01T00:00:00Z2024-05-22T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10400.5/29475engLashkajani, Masoud Mirzakazemi (2023). “Predictive modeling of motor client's propensity to churn”. Dissertação de Mestrado. Universidade de Lisboa. Instituto Superior de Economia e Gestãoinfo:eu-repo/semantics/embargoedAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-11-26T01:32:03Zoai:www.repository.utl.pt:10400.5/29475Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T23:19:48.744417Repositório 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 Predictive modeling of motor client's propensity to churn
title Predictive modeling of motor client's propensity to churn
spellingShingle Predictive modeling of motor client's propensity to churn
Lashkajani, Masoud Mirzakazemi
Supervised Learning
Classification
Customer Churn Prediction
Predictive model
K-Neighbors Classifier
Decision Tree
Logistic Regression
Random Forest
Extreme Gradient Boosting
LightGBM
title_short Predictive modeling of motor client's propensity to churn
title_full Predictive modeling of motor client's propensity to churn
title_fullStr Predictive modeling of motor client's propensity to churn
title_full_unstemmed Predictive modeling of motor client's propensity to churn
title_sort Predictive modeling of motor client's propensity to churn
author Lashkajani, Masoud Mirzakazemi
author_facet Lashkajani, Masoud Mirzakazemi
author_role author
dc.contributor.none.fl_str_mv Bastos, João
Franco, Luís
Silva, Paulo Martins
Repositório da Universidade de Lisboa
dc.contributor.author.fl_str_mv Lashkajani, Masoud Mirzakazemi
dc.subject.por.fl_str_mv Supervised Learning
Classification
Customer Churn Prediction
Predictive model
K-Neighbors Classifier
Decision Tree
Logistic Regression
Random Forest
Extreme Gradient Boosting
LightGBM
topic Supervised Learning
Classification
Customer Churn Prediction
Predictive model
K-Neighbors Classifier
Decision Tree
Logistic Regression
Random Forest
Extreme Gradient Boosting
LightGBM
description Mestrado Bolonha em Data Analytics for Business
publishDate 2023
dc.date.none.fl_str_mv 2023-03
2023-03-01T00:00:00Z
2024-05-22T00:00:00Z
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/10400.5/29475
url http://hdl.handle.net/10400.5/29475
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Lashkajani, Masoud Mirzakazemi (2023). “Predictive modeling of motor client's propensity to churn”. Dissertação de Mestrado. Universidade de Lisboa. Instituto Superior de Economia e Gestão
dc.rights.driver.fl_str_mv info:eu-repo/semantics/embargoedAccess
eu_rights_str_mv embargoedAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Instituto Superior de Economia e Gestão
publisher.none.fl_str_mv Instituto Superior de Economia e Gestão
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
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