[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121459-en":3,"doc-seo-121459-105":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},121459,13056703019662,"Evangeline","https://ap-avatar.wpscdn.com/avatar/be000253a8e92610077?_k=1778726343310543188",8,"Research & Report","Machine Learning Approaches for Customer Churn Prediction - Balancing Accuracy and Interpretability - Thesis","The study focuses on customer churn in the telecommunications industry, where keeping existing users is far more cost-effective than acquiring new ones. It applies machine learning to predict churn using demographic, contractual, service, and billing data, evaluating interpretable models such as Logistic Regression and Decision Trees alongside advanced methods including Random Forest, Gradient Boosting, SVM, and Neural Networks. Results emphasize predictive performance, interpretability, key churn drivers, and trade-offs, supporting data-driven retention strategy design.","UCLA  \nUCLA Electronic Theses and Dissertations  \nTitle  \nMachine Learning Approaches for Customer Churn Prediction: Balancing Accuracy and Interpretability  \nPermalink  \n[https://escholarship.org/uc/item/5h44m3rc](https://escholarship.org/uc/item/5h44m3rc)  \nISBN  \n9798293838837  \nAuthor  \nChen, Jack  \nPublication Date  \n2025-09-08  \nPeer reviewed|Thesis/dissertation  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \nUNIVERSITY OF CALIFORNIA  \nLos Angeles  \nMachine Learning Approaches for Customer Churn Prediction: Balancing Accuracy and  \nInterpretability  \nA thesis submitted in partial satisfaction  \nof the requirements for the degree  \nMaster of Applied Statistics and Data Science  \nby  \nJack Chen  \n© Copyright by Jack Chen  \n2025  \nABSTRACT OF THE THESIS  \nMachine Learning Approaches for Customer Churn Prediction: Balancing Accuracy and  \nInterpretability  \nby  \nJack Chen  \nMaster of Applied Statistics and Data Science  \nUniversity of California, Los Angeles, 2025  \nYing Nian Wu, Chair  \nThe study addresses the problem of customer churn in the telecommunications industry, where retaining existing users is significantly more cost-effective than acquiring new ones. It investigates the application of machine learning techniques for churn prediction using demographic, contractual, service, and billing information. A range of models are evaluated, from interpretable approaches such as Logistic Regression and Decision Trees to advanced methods including Random Forest, Gradient Boosting, Support Vector Machines, and Neural Networks. The analysis emphasizes predictive performance and interpretability, identifies key factors driving churn, and discusses trade-offs among different approaches. The findings provide both methodological insights into the use of machine learning for churn prediction and practical guidance for developing data-driven strategies to improve customer retention.  \nThe thesis of Jack Chen is approved.  \nMaria Cha  \nXiaowu Dai  \nHongquan Xu Yingnian Wu, Committee Chair  \nUniversity of California, Los Angeles 2025  \nContents  \nAbstract ii  \nList of Figures v  \nList of Tables vi  \n1 Introduction 1  \n1.1 Background and Motivation ........................... 1  \n1.2 Problem Setting .................................. 2  \n1.3 Research Objectives and Contributions ..................... 2  \n2 Data Preparation 4  \n2.1 Dataset Description ................................ 4  \n2.2 Data Preprocessing ................................ 6  \n3 Exploratory Data Analysis 7  \n3.1 Descriptive Statistics ............................... 7  \n3.2 Categorical Variables Analysis .......................... 8  \n3.3 Numerical Variables Analysis .......................... 9  \n3.4 Correlation Analysis ............................... 11  \n4 Methodology 13  \n4.1 Modeling ...................................... 13  \n4.2 Baseline Models and Results ........................... 14  \n4.3 Tree-Based Models and Results ......................... 17  \n4.4 Advanced Models and Results .......................... 21  \n5 Results and Comparison 28  \n5.1 Overview ...................................... 28  \n5.2 Model Comparison ................................ 29  \n5.3 Trade-off Discussion ................................ 30  \n5.4 Key Findings ................................... 31  \n6 Discussion 32  \n6.1 Interpretation and Business Implications .................... 32  \n6.2 Limitations and Future Work .......................... 33  \n6.3 Conclusion ..................................... 34  \nList of Figures  \n3.1 Customer distribution across demographic groups (gender, senior citizen status, partner, and dependents) and their relationship with churn.......... 8  \n3.2 Churn distribution across categorical variables: Contract, InternetService, OnlineSecurity, and TechSupport......................... 9  \n3.3 Distribution of monthly charges across churn categories............. 10  \n3.4 Distribution of total ch","cbCainTMnDBoKdm5","https://ap.wps.com/l/cbCainTMnDBoKdm5","pdf",2511862,1,45,"English","en",105,"# Introduction\n## Background and Motivation\n## Problem Setting\n## Research Objectives and Contributions\n# Data Preparation\n## Dataset Description\n## Data Preprocessing\n# Exploratory Data Analysis\n## Descriptive Statistics\n## Categorical Variables Analysis\n## Numerical Variables Analysis\n## Correlation Analysis\n# Methodology\n## Modeling\n## Baseline Models and Results\n## Tree-Based Models and Results\n## Advanced Models and Results\n# Results and Comparison\n## Overview\n## Model Comparison\n## Trade-off Discussion\n## Key Findings\n# Discussion\n## Interpretation and Business Implications\n## Limitations and Future Work\n## Conclusion","[{\"question\":\"What problem does the thesis address?\",\"answer\":\"It addresses customer churn prediction, especially in the telecommunications industry, where retaining customers is cheaper than acquiring new ones.\"},{\"question\":\"Which types of features are used for churn prediction?\",\"answer\":\"The models use demographic, contractual, service, and billing information.\"},{\"question\":\"How does the thesis compare different machine learning models?\",\"answer\":\"It evaluates interpretable approaches like Logistic Regression and Decision Trees and compares them with advanced models such as Random Forest, Gradient Boosting, SVM, and Neural Networks, focusing on both predictive performance and interpretability.\"}]","Machine Learning Approaches for Customer Churn Prediction - Balancing Accuracy and Interpretability - Thesis | PDF",1785735752,113,{"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},"machine-learning-approaches-for-customer-churn-prediction-balancing-accuracy-and-interpretability-thesis","",{"@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/machine-learning-approaches-for-customer-churn-prediction-balancing-accuracy-and-interpretability-thesis/121459/",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 thesis address?","Question",{"text":75,"@type":76},"It addresses customer churn prediction, especially in the telecommunications industry, where retaining customers is cheaper than acquiring new ones.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which types of features are used for churn prediction?",{"text":80,"@type":76},"The models use demographic, contractual, service, and billing information.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the thesis compare different machine learning models?",{"text":84,"@type":76},"It evaluates interpretable approaches like Logistic Regression and Decision Trees and compares them with advanced models such as Random Forest, Gradient Boosting, SVM, and Neural Networks, focusing on both predictive performance and interpretability.","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"]