[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120950-en":3,"doc-seo-120950-105":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":4,"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},120950,549758252649,"Ivy","https://ap-avatar.wpscdn.com/avatar/8000253669c5317157?_k=1778319167496531819",8,"Research & Report","Customer Churn Prediction In Banking Industries - Supervised Machine Learning Approach - Dissertation","Customer churn in the banking industry occurs when clients end their relationship with a bank, causing substantial revenue loss and reputational impact. In a highly competitive market, retaining customers is essential to sustain income and generate growth through trust and referrals. The study applies multiple supervised machine learning algorithms to predict churn risk, then analyzes the models to extract early warning patterns. Findings support actionable recommendations for banks to improve customer retention strategies.","UCLA  \nUCLA Electronic Theses and Dissertations  \nTitle  \nCustomer Churn Prediction In Banking Industries: Supervised Machine Learning Approach  \nPermalink  \n[https://escholarship.org/uc/item/205660xs](https://escholarship.org/uc/item/205660xs)  \nAuthor  \nJIANG, SUJIAN  \nPublication Date  \n2024  \nPeer reviewed|Thesis/dissertation  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \nUNIVERSITY OF CALIFORNIA  \nLos Angeles  \nCustomer Churn Prediction In Banking Industries: Supervised Machine Learning Approach  \nA dissertation submitted in partial satisfaction of the Requirements for the degree of Master of Applied Statistics & Data Science  \nby  \nSUJIAN JIANG  \n© Copyright by SUJIAN JIANG  \n2024  \nABSTRACT OF THE DISSERTATION  \nCustomer Churn Prediction  \nIn Banking Industries: Supervised Machine  \nLearning Approach  \nby  \nSUJIAN JIANG  \nMaster of Applied Statistics & Data Science University of California, Los Angeles, 2024  \nProfessor YingNian Wu, Chair  \nCustomer churn in the banking industry occurs when clients terminate their relationship with the bank, leading to significant losses in revenue and reputation. In today's highly competitive market, retaining customers is crucial. A strong customer base not only sustains the bank's revenue but also attracts new clients through trust and referrals from satisfied customers. Therefore, identifying and preventing customer churn is a critical task for banks. Our research utilized various machine learning algorithms to predict which customers are likely to leave. By analyzing the models, we identified patterns that serve as early warning signs of churn. Based on these valuable insights, we provide banks with recommendations on effective strategies to retain their customers.  \nThe dissertation of SUJIAN JIANG is approved.  \nNicolas Christou  \nOscar Madrid Padilla Ying Nian Wu, Committee Chair  \nUniversity of California, Los Angeles 2024  \nTable of Contents  \n1 Introduction ....................................................... 1  \n2 Methodology ...................................................... 4  \n2.1 XGBoost ...................................................... 4  \n2.2 Random Forest ................................................. 5  \n2.3 Support Vector Machine (SVM) ..................................... 6  \n2.4 AdaBoost ...................................................... 7  \n3 Data and Data Preprocessing ......................................... 9  \n4 Exploratory Data Analysis (EDA) ..................................... 11  \n4.1 Distribution of the Target by Categorical Variables ..................... 12  \n4.1.1 Geography and Target ...................................... 12  \n4.1.2 Gender and Target ......................................... 13  \n4.1.3 Has Credit Card and Target .................................. 13  \n4.1.4 IsActiveMember and Target .................................. 14  \n4.2 Distribution of the Target by Numerical Variables ...................... 15  \n4.2.1 Credit Score and Target ..................................... 15  \n4.2.2 Estimated Salary and Balance ................................ 17  \n4.2.3 Age and the Target ......................................... 18  \n4.2.4 Number of Products and Tenure ............................... 20  \n4.3 EDA Conclusion ................................................ 22  \n5 Statistical Modeling ................................................ 24  \n5.1 Model Evaluation ............................................... 24  \n5.2 Validation and Hyperparameters ................................... 25  \n5.3 Model Result .................................................. 28  \n5.3.1 Predictive Performance ...................................... 28  \n5.3.2 Feature Importance Analysis ................................. 29  \n6 Conclusion and Future Work ........................................ 31  \n7 References ....................................................... 33  \nList of ","cbCaiu5UAsjoaE0c","https://ap.wps.com/l/cbCaiu5UAsjoaE0c","pdf",799236,1,43,"English","en",105,"# 1 Introduction\n# 2 Methodology\n## 2.1 XGBoost\n## 2.2 Random Forest\n## 2.3 Support Vector Machine (SVM)\n## 2.4 AdaBoost\n# 3 Data and Data Preprocessing\n# 4 Exploratory Data Analysis (EDA)\n## 4.1 Distribution of the Target by Categorical Variables\n## 4.2 Distribution of the Target by Numerical Variables\n# 5 Statistical Modeling\n## 5.1 Model Evaluation\n## 5.2 Validation and Hyperparameters\n## 5.3 Model Result\n# 6 Conclusion and Future Work\n# 7 References","[{\"question\":\"What problem does the dissertation address in the banking industry?\",\"answer\":\"It addresses customer churn, where clients stop using a bank’s services or move to competitors, leading to revenue and reputation losses.\"},{\"question\":\"Which supervised machine learning models are used to predict churn?\",\"answer\":\"The study evaluates XGBoost, Random Forest, Support Vector Machine (SVM), and AdaBoost for predicting which customers are likely to leave.\"},{\"question\":\"How does the research use model analysis to support churn prevention?\",\"answer\":\"By analyzing the models, the research identifies patterns that act as early warning signs of churn, then translates those insights into recommendations for retention strategies.\"}]","Customer Churn Prediction In Banking Industries - Supervised Machine Learning Approach - Dissertation | PDF",1785733002,108,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"customer-churn-prediction-in-banking-industries-supervised-machine-learning-approach-dissertation","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/customer-churn-prediction-in-banking-industries-supervised-machine-learning-approach-dissertation/120950/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does the dissertation address in the banking industry?","Question",{"text":75,"@type":76},"It addresses customer churn, where clients stop using a bank’s services or move to competitors, leading to revenue and reputation losses.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which supervised machine learning models are used to predict churn?",{"text":80,"@type":76},"The study evaluates XGBoost, Random Forest, Support Vector Machine (SVM), and AdaBoost for predicting which customers are likely to leave.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the research use model analysis to support churn prevention?",{"text":84,"@type":76},"By analyzing the models, the research identifies patterns that act as early warning signs of churn, then translates those insights into recommendations for retention strategies.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]