[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121046-en":3,"doc-seo-121046-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},121046,2336464648322,"Aria","https://ap-avatar.wpscdn.com/avatar/2200025388227c56fec?_k=1778556882303663488",8,"Research & Report","Customer Churn Prediction in Portuguese Banking Sector - Using a Machine Learning Approach","This study develops a predictive model for customer churn in a Portuguese bank using machine learning techniques. The workflow follows the CRISP-DM methodology, combining extensive exploratory data analysis, data preparation, and visualizations to support model selection. Among evaluated approaches, tree-based and ensembled methods are tested, with Gradient Boosting delivering standout predictive performance. The model enables identification of high-risk customers and supports proactive retention planning. An interactive Power BI dashboard is built to help stakeholders act on churn risk, improving decision-making and business outcomes.","MGI  \nMaster Degree Program in  \nInformation Management  \nCUSTOMER CHURN PREDICTION IN PORTUGUESE BANKING  \nSECTOR  \nUsing a Machine Learning Approach  \nInês Tomás Pires  \nMaster Thesis  \npresented as partial requirement for obtaining the Master Degree Program in Information Management  \nNOVA Information Management School Instituto Superior de Estatística e Gestão de Informação  \nUniversidade Nova de Lisboa  \nNOVA Information Management School Instituto Superior de Estatística e Gestão de Informação  \nUniversidade Nova de Lisboa  \nCUSTOMER CHURN PREDICTION IN PORTUGUESE BANKING SECTOR  \nUsing a Machine Learning Approach  \nby  \nInês Tomás Pires  \nMaster Thesis presented as partial requirement for obtaining the Master’s degree in Information Management, with a specialization in Knowledge Management and Business Intelligence  \nSupervised by  \nMiguel de Castro Simões Ferreira Neto, PhD, NOVA Information Management School João Bruno Morais de Sousa Jardim, PhD, NOVA Information Management School  \nDecember, 2023  \nSTATEMENT OF INTEGRITY  \nI hereby declare having conducted this academic work with integrity. I confirm that I have not used plagiarism or any form of undue use of information or falsification of results along the process leading to its elaboration. I further declare that I have fully acknowledged the Rules of Conduct and Code of Honor from the NOVA Information Management School.  \nInês Tomás Pires  \nLisbon, 4th December 2023  \nACKNOWLEDGEMENTS  \nFirst, I would like to show my gratitude to my supervisors, professors Bruno Jardim and Miguel Neto, for all the support and comprehension in the development of this work and for believing in the potential of this study.  \nSecond, I want to thank José Coelho and João Gomes, the JJ’s, for being my life saviors and helping me with the dilemmas throughout this dissertation. It wouldn’t have been possible without their help and a little bit of their fun.  \nI also want to give special thanks to Leandro Gonçalves for showing me his passion for data and for all the knowledge I gather with him every day. His passion became mine too.  \nLast but (definitely) not least, to Patrícia Ferreira, for dealing with all my doubts and insecurities and for believing in me. Thank you for always being there and for being my shelter.  \nABSTRACT  \nThis 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.  \nKEYWORDS  \nBanking Sector; Business Intelligence; Customer Churn; Machine Learning; Power BI  \nSustainable Development Goals (SDG):  \nTABLE OF CONTENTS  \nStatement of Integrity ........................................................................................................ i  \nAcknowledgements ........................................................................................................... ii  \nAbstract ............................................................................................................................ iii  \nList of [Figures........................................................................","cbCaib5fkPOAZRF1","https://ap.wps.com/l/cbCaib5fkPOAZRF1","pdf",1802549,1,50,"English","en",105,"# Introduction\n## Background and Problem Identification\n## Study Objectives\n## Study Relevance and Importance\n# Literature review\n## Customer Churn Definition\n### Churn in banking sector\n### Case studies in banking sector\n## Predictive Models\n### Machine Learning Models in Customer Churn\n# Methodology\n## CRISP-DM\n### Business Understanding","[{\"question\":\"Which methodology guides the churn analysis in this study?\",\"answer\":\"The study follows the CRISP-DM methodology, covering business understanding, data preparation, and model development supported by EDA and visualizations.\"},{\"question\":\"What type of models performs best in predicting customer churn?\",\"answer\":\"Tree-based and ensembled models are evaluated, and Gradient Boosting is identified as the standout performer with notable predictive capability.\"},{\"question\":\"How are the results delivered to support proactive customer retention?\",\"answer\":\"Beyond identifying customers at risk, the study develops an interactive dashboard (Power BI) so stakeholders can address potential churn risks with better decision-making.\"}]","Customer Churn Prediction in Portuguese Banking Sector - 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