[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119617-en":3,"doc-seo-119617-105":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},119617,1099514068365,"Aurelia","https://ap-avatar.wpscdn.com/avatar/10000253d8d9f28188e?_k=1776742907772140068",8,"Research & Report","Predicting Customer Churn in Telecom Companies - A Machine-Learning Approach","Research examines how machine learning methods can forecast customer churn in telecommunication companies. Models including extreme gradient boosting, random forest, k-nearest neighbour, adaptive boosting, support vector machine, and logistic regression are trained, compared, and used to analyse churn behaviour. Cross-validation is employed to improve performance, highlighting contract length, customer tenure, and service usage patterns as key churn predictors. Findings show machine learning can identify potential churners accurately, supporting predictive analytics for proactive retention actions.","PREDICTING CUSTOMER CHURN IN TELECOM COMPANIES THROUGH A MACHINE-LEARNING APPROACH  \nBY  \nHLAYISANI RESULT KHOZA  \nDISSERTATION  \nSubmitted in fulfillment of the requirements for the degree of  \nMASTER OF SCIENCE  \nin  \nE-SCIENCE  \nin the  \nFACULTY OF SCIENCE AND AGRICULTURE  \n(School of Mathematical and Computer Sciences)  \nat the  \nUNIVERSITY OF LIMPOPO  \nSUPERVISOR: Dr. TB Darikwa  \n18 June 2024  \nDeclaration  \nI, Hlayisani Result Khoza, hereby certify that this dissertation is submitted to the University of Limpopo in partial fulfillment of the requirements for the degree of Master of Science( E-Science) and is an original work of mine, that I have properly acknowledged all of the material in it, and that I have not submitted it for credit toward any degree at this or any other university.  \nSignature:........HR..............Date:...05 June 2024 ............................  \nKhoza, H.R.  \nAbstract  \nThe research inspects the application of machine learning approaches to forecasting customer attrition in telecommunication companies. Machine learning models such as extreme gradient boosting, random forest, k-nearest neighbour, adaptive boosting, support vector machine, and logistic regression were used to forecast and compared the best model and analysed churn behaviour. Cross-validation techniques were applied to enhance model performance, revealing critical predictors of churn such as contract length, customer tenure, and service usage patterns. The results emphasised the effectiveness of machine learning in accurately identifying potential churners. Furthermore, the study emphasises the importance of leveraging predictive analytics to proactively address customer attrition, enabling telecommunication companies to devise targeted retention strategies and enhance customer satisfaction and loyalty.  \nDedication  \nDedication:  \nI dedicate this master research to the unwavering support and boundless love of my family, whose encouragement has been the anchor of my academic journey. To my friends and mentors, your guidance and insights have illuminated my path, shaped the course of my research, and fostered intellectual growth.  \nThis endeavor is a testament to the resilience and dedication of my professorsand colleagues, whose collective efforts have enriched my academic experience. Above all, I dedicate this research to the pursuit of knowledge and the countless individuals around the world whose stories, struggles, and triumphs inspire me to contribute meaningfully to the field.  \nAcknowledgments  \nI extend my heartfelt gratitude to my supervisor, Dr. TB Darikwa, for guiding me through this research project. I am deeply thankful for his mentorship and support. May the Almighty God continue to bless him. I also wish to express my special thanks to the National e-Science Postgraduate Teaching and Training Platform (NEPTTP) for their sponsorship.  \nContents  \nDeclaration i  \nAbstract ii  \nDedication iii  \nAcknowledgments iv  \nTable of Contents v  \nList of Figures viii  \n1 Introduction 1  \n1.1 Introduction ............................... 1  \n1.2 Background ............................... 3  \n1.3 Problem statement ........................... 4  \n1.4 Rationale ................................. 5  \n1.5 Aim and objectives ........................... 6  \n1.5.1 Aim ................................ 6  \n1.5.2 Objectives ............................ 7  \n1.6 Significance of the study ........................ 7  \n1.7 Structure of the dissertation ...................... 7  \n2 Literature review 9  \n2.1 Introduction ............................... 9  \n2.1.1 K-nearest neighbour ...................... 10  \n2.1.2 Support vector machine .................... 10  \n2.1.3 Random forest classifier .................... 11  \n2.1.4 Logistic regression ....................... 12  \n2.1.5 Extreme gradient boosting ................... 14  \n2.1.6 Adaptive boosting ........................ 14  \n2.2 Review of the application of ML algorithms to customer churn a","cbCaipzYq9zjl8Ql","https://ap.wps.com/l/cbCaipzYq9zjl8Ql","pdf",635396,1,69,"English","en",105,"# Introduction\n## Background\n## Problem statement\n## Rationale\n## Aim and objectives\n## Significance of the study\n## Structure of the dissertation\n# Literature review\n## K-nearest neighbour\n## Support vector machine\n## Random forest classifier\n## Logistic regression\n## Extreme gradient boosting\n## Adaptive boosting\n# Methodology\n## Data source and study area\n## Overall research approach and design\n## Machine learning data analysis approach\n## Evaluation techniques\n# Results and discussion\n## Exploratory data analysis\n## Model performance comparison\n# Conclusion\n## Recommendations","[{\"question\":\"Which machine learning models are used to predict customer churn?\",\"answer\":\"The study uses extreme gradient boosting, random forest, k-nearest neighbour, adaptive boosting, support vector machine, and logistic regression to forecast and compare churn performance.\"},{\"question\":\"How does the research improve model performance and what predictors are identified?\",\"answer\":\"Cross-validation is applied to enhance performance. The results identify contract length, customer tenure, and service usage patterns as critical predictors of churn.\"},{\"question\":\"What is the practical value of the churn prediction results for telecom companies?\",\"answer\":\"The findings support proactive use of predictive analytics to identify likely churners and design targeted retention strategies that improve satisfaction and loyalty.\"}]","Predicting Customer Churn in Telecom Companies - A Machine-Learning Approach | PDF",1785725326,174,{"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},"predicting-customer-churn-in-telecom-companies-a-machine-learning-approach","",{"@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/predicting-customer-churn-in-telecom-companies-a-machine-learning-approach/119617/",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},"Which machine learning models are used to predict customer churn?","Question",{"text":75,"@type":76},"The study uses extreme gradient boosting, random forest, k-nearest neighbour, adaptive boosting, support vector machine, and logistic regression to forecast and compare churn performance.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the research improve model performance and what predictors are identified?",{"text":80,"@type":76},"Cross-validation is applied to enhance performance. The results identify contract length, customer tenure, and service usage patterns as critical predictors of churn.",{"name":82,"@type":73,"acceptedAnswer":83},"What is the practical value of the churn prediction results for telecom companies?",{"text":84,"@type":76},"The findings support proactive use of predictive analytics to identify likely churners and design targeted retention strategies that improve satisfaction and loyalty.","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"]