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Random Forest achieves the highest accuracy, precision, and F-measure.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/research-report/","Research & Report",3,{"item":32,"name":10,"@type":21,"position":33},"https://docshare.wps.com/document/identifying-customer-churn-in-telecom-sector-a-machine-learning-approach/290467/",4,{"url":32,"name":10,"@type":35,"image":36,"author":41,"headline":10,"publisher":44,"fileFormat":47,"inLanguage":8,"description":12,"dateModified":48,"datePublished":49,"encodingFormat":47,"isAccessibleForFree":50,"interactionStatistic":51},"DigitalDocument",{"url":37,"@type":38,"width":39,"height":40},"https://docshare.wps.com/thumbnails/identifying-customer-churn-in-telecom-sector-a-machine-learning-approach/290467.png","ImageObject",300,407,{"name":42,"@type":43},"Violet","Person",{"url":19,"name":45,"@type":46},"DocShare","Organization","application/pdf","2026-10-30","2026-09-17",true,{"@type":52,"interactionType":53,"userInteractionCount":55},"InteractionCounter",{"@type":54},"ViewAction",10,{"@type":57,"mainEntity":58},"FAQPage",[59,65,69],{"name":60,"@type":61,"acceptedAnswer":62},"Why is customer churn a major concern in the telecom sector?","Question",{"text":63,"@type":64},"Telecom markets are highly competitive and customers have many service options. This drives churn and makes retention a top priority for providers.","Answer",{"name":66,"@type":61,"acceptedAnswer":67},"Which machine learning models are compared in the study?",{"text":68,"@type":64},"The paper compares Support Vector Machine (SVM), Decision Tree (DT), and Random Forest (RF) for churn prediction.",{"name":70,"@type":61,"acceptedAnswer":71},"How does the paper prepare features and evaluate the models?",{"text":72,"@type":64},"It uses chi-square for feature selection and validates performance with a ten-fold cross-validation approach.","https://schema.org",{"og:url":32,"og:type":75,"og:title":10,"og:site_name":45,"og:description":12},"article",{"robots":77,"canonical":32},"index,follow",{"doc_id":79,"site_id":7},290467,1790524612,{"code":4,"msg":82,"data":83},"success",[84,88,92,96,101,106,111,115,120,123,126],{"id":22,"doc_module":4,"doc_module_name":25,"category_name":85,"show_sort_weight":86,"slug":87},"Story & Novel",90,"story-novel",{"id":26,"doc_module":4,"doc_module_name":25,"category_name":89,"show_sort_weight":90,"slug":91},"Literature",80,"literature",{"id":33,"doc_module":4,"doc_module_name":25,"category_name":93,"show_sort_weight":94,"slug":95},"Exam",70,"exam",{"id":97,"doc_module":4,"doc_module_name":25,"category_name":98,"show_sort_weight":99,"slug":100},5,"Comic",60,"comic",{"id":102,"doc_module":4,"doc_module_name":25,"category_name":103,"show_sort_weight":104,"slug":105},6,"Technology",50,"technology",{"id":107,"doc_module":4,"doc_module_name":25,"category_name":108,"show_sort_weight":109,"slug":110},7,"Healthcare",40,"healthcare",{"id":112,"doc_module":4,"doc_module_name":25,"category_name":29,"show_sort_weight":113,"slug":114},8,30,"research-report",{"id":116,"doc_module":4,"doc_module_name":25,"category_name":117,"show_sort_weight":118,"slug":119},9,"Religion & Spirituality",20,"religion-spirituality",{"id":118,"doc_module":4,"doc_module_name":25,"category_name":121,"show_sort_weight":118,"slug":122},"World Cup","world-cup",{"id":55,"doc_module":4,"doc_module_name":25,"category_name":124,"show_sort_weight":55,"slug":125},"Lifestyle","lifestyle",{"id":127,"doc_module":4,"doc_module_name":25,"category_name":128,"show_sort_weight":97,"slug":129},19,"General","general",{"code":4,"msg":82,"data":131},{"doc_id":79,"user_id":132,"nickname":42,"user_avatar":133,"doc_module":4,"category_id":112,"category_name":29,"doc_title":10,"doc_description":12,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":55,"is_deleted":4,"is_public":22,"is_downloadable":22,"audit_status":22,"page_count":139,"language":140,"language_code":8,"site_id":7,"html_lang":8,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":12,"update_tm":144,"read_time":109,"preview_page_count":4},4398048950312,"https://ap-avatar.wpscdn.com/avatar/400002538284de19e3c?_k=1778320343897328908","DOI: 10.53704/fujnas.v13i2 .469  \nA publication of College of Natural and Applied Sciences, Fountain University, Osogbo, Nigeria.  \n[Journal homepage: www.fountainjournals.com](Journal homepage: www.fountainjournals.com)  \n[ISSN: 2354-337X](ISSN: 2354-337X)(Online),2350-1863(Print)  \nIdentifying Customer Churn in Telecom Sector: A Machine Learning Approach  \n*Hambali A. M., Emmanuel L., Olasupo Y. A., Ishaku A  \nFederal University Wukari, PMB 1020, Katsina-Ala Road, Wukari, Nigeria  \nAbstract  \nNowadays, there is no shortage of options for customers when choosing where to put their money. As a result, customer churn and engagement have become one of the top issues. With the increase in the number of service providers for the same targeted population, there is a need for service providers to try to find the changing customer behaviour and their rising expectations to retain them. Various studies have proposed customer churn. Data mining was routinely used to predict telecom customer attrition. Most researchers have compared and proposed different approaches for the prediction of customer churn, though, some of the Machine learning (ML) algorithms used were unable to provide the performance needed to identify customer churn. Therefore this paper presents a comparative analysis of Support Vector Machine (SVM), Decision Tree (DT) and Random Forest (RF) in Telecommunications Dataset. To prepare the dataset for machine learning algorithms, chi-square was used for feature selection to select the most informative features from the original dataset. We validate our model using a ten-fold cross-validation approach to test the performance of our models. RF model performed better than other models in terms of accuracy (94%), precision (94%) and F-measure (94%) respectively. Additionally, we compared our results with existing models that used the same dataset, the proposed strategy outperformed them.  \nKeywords: Customer Churn, Telecommunication, Machine Learning, Support Vector Machine (SVM),  Decision Tree (DT), Random Forest (RF)   \nIntroduction  \nThe telecommunication market is very dynamic and highly competitive nowadays. The number of service providers has increased very rapidly in every business (Rahman & Kumar, 2020) . The challenges of service providers are finding the changing customer behaviour and their rising expectations. These days, there is no shortage of options for customers when choosing where to put their money or chosen service provider. As a result, customer churn and engagement have become one of the top issues. Therefore, this is a  \nbig challenge for the new generation telecommunication service providers to think innovatively to fulfil and add value to the customers.  \nSeveral scientific journals and corporate publications report exciting developments in technology and competition in the mobile telecoms sector. Fujo et al. (2022) reported that Telecommunications has long been part ofour * Corresponding Author:[https://orcid.org/0000-0002-2194-9376](https://orcid.org/0000-0002-2194-9376)[ ](https://orcid.org/0000-0002-2194-9376)Email address: [hambali@fuwukari.edu.ng](hambali@fuwukari.edu.ng)  \nculture but is attracting more attention now than ever before. A compelling case is the General Packet Radio Service (GPRS) - based medium broadband networks’ rapid growth, which now hit a market penetration level of 70–80 per cent in many European Union (EU) countries.  \nAn important discipline within database marketing is customer retention management (CRM), or the prevention of customer churn, defined as the propensity of customers to end the relationship with the company, and to switch to the competition. Churn prediction is the process of using transaction data to identify customers who are likely to cease their relationship with a company (Leung & Chung, 2020) .  \nNowadays, CRM systems are progressively utilizing the potential of machine learning (ML) to forecast customer churn. By scrutinizing historical customer data ","cbCaijZZiphmnTip","https://ap.wps.com/l/cbCaijZZiphmnTip","pdf",834234,16,"English","# Introduction\n## Customer churn and customer retention context\n## Machine learning for churn prediction\n## Related approaches and study motivation\n# Method Overview\n## Feature selection and dataset preparation\n## Comparative modeling: SVM, DT, RF\n## Evaluation via ten-fold cross-validation","[{\"question\":\"Why is customer churn a major concern in the telecom sector?\",\"answer\":\"Telecom markets are highly competitive and customers have many service options. This drives churn and makes retention a top priority for providers.\"},{\"question\":\"Which machine learning models are compared in the study?\",\"answer\":\"The paper compares Support Vector Machine (SVM), Decision Tree (DT), and Random Forest (RF) for churn prediction.\"},{\"question\":\"How does the paper prepare features and evaluate the models?\",\"answer\":\"It uses chi-square for feature selection and validates performance with a ten-fold cross-validation approach.\"}]","Identifying Customer Churn in Telecom Sector - A Machine Learning Approach | PDF",1789641750]