[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125123-en":3,"doc-seo-125123-105":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},125123,7971461740909,"Levi","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Leveraging Machine Learning Models for Customer Churn Prediction in Telecommunications - Insights and Implications","Telecommunications customer churn undermines profits and weakens loyalty over time, making accurate prediction essential for sustained competitiveness. This study evaluates three machine learning approaches—Random Forest, CatBoost, and K-Nearest Neighbors—using a dataset of 7,043 customer histories with 21 attributes. Random Forest delivers the strongest results (99% accuracy, 99% F1 score, 0.99 AUC, and 88% recall), enabling proactive identification of at-risk customers. The findings support data-driven decision pathways for targeted retention actions and improved customer satisfaction.","VAWKUM Transactions on Computer Sciences  \n[http://vfast.org/journals/index.php/VTCS@ 2024](http://vfast.org/journals/index.php/VTCS@ 2024), ISSN(e): 2308-8168, ISSN(p): 2411-6335  \nVolume 12, Number 2, July-December 2024 pp:16-27  \nKeywords: Customer Churn Prediction, Telecommunications Industry, Machine Learning Models, Random Forest, Customer Retention, Predictive Analytics.  \nJournal Info:  \nSubmitted: September 12, 2024 Accepted: September 27, 2024 Published:  \nOctober 09, 2024  \nLeveraging Machine Learning Models for Customer Churn Prediction in Telecommunications: Insights and Implications  \nJamil Ahmed1* , Ilyas Younus1 , Usama Sarwar2 , Rashid Ghaffar3 , Tufail Ahmed1  \n1 Department of Computer Science, Faculty of Engineering Science & Technology, Iqra University Karachi, Pakistan; 2 Pakistan Scientific & Technological Information Centre (PASTIC); 3Tekvek, Karachi, Sindh, Pakistan  \nAbstract In the world of telecommunications businesses, customer turnover poses a significant hurdle that can impact profits and weaken customer loyalty over time. Our solution to this challenge involves a method using Machine Learning (ML) tools to predict churn, with precision. We work with a set of 7In our research study we examined how well three different machine learning models performed. Random Forest (RF) Cat Boost (CB) and K nearest neighbors (KNN) . Out of these models tested the Random Forest model stood out for its performance achieving 99 percent accuracy and precision along with an 88 percent recall rate and a 99 percent F1 score; additionally, it achieved an AUCof 0.99 . These results clearly demonstrate the Random Forest model’s ability, in identifying customers who are likely to churn. The findings of this study hold importance for telecommunications companies as they are equipped with a valuable resource to proactively tackle customer turnover issues and customize solutions to retain key clients while boosting overall customer happiness levels in an increasingly competitive market landscape where keeping customers is crucial for business success our research provides a data supported roadmap for continual expansion and staying ahead in the telecom industry spotlighted in this abstract is the critical relevance of churn prediction for telecom firms underscored by the tangible advantages of leveraging the Random Forest model for predicting customer churn. By utilizing this advanced technology, telecom companies can proactively identify at-risk customers and take targeted measures to prevent them from leaving. This not only helps to retain key clients but also improves overall customer satisfaction. In a constantly evolving market, having access to predictive analytics can give companies a significant edge and ensure long-term success in the industry.  \n*Correspondence author email address: [Jamil.ahmed@iqra.edu.pk](Jamil.ahmed@iqra.edu.pk)[ ](Jamil.ahmed@iqra.edu.pk)DOI: 10.21015/vtcs.v12i2 .1904  \nThis work is licensed under a Creative Commons Attribution 3.0 License.  \nVAWKUM Transactions on Computer Sciences Volume 12, Issue 2, 2024  \n1 Introduction  \nIn the highly competitive telecommunications industry, customer churn has become a major challenge to continued success, and with rapid technological advancements, losing customers not only results in a major financial setback but also damages a telecommunications company’s brand reputation. With so many choices available to consumers and the ability to switch providers with just a few clicks, telecommunications companies face an uphill battle to gain customer loyalty.The consequences of losing this battle are grave: revenue decline, brand degradation, and a reduction in market share. In response to the critical need for advanced solutions to mitigate churn, this paper presents a pioneering approach to churn prediction, leveraging the power of data-driven methodologies and cutting-edge machine learning (ML) algorithms [1], The telecommunications industry is an active ecosystem cha","cbCaitsKQQmmJGaz","https://ap.wps.com/l/cbCaitsKQQmmJGaz","pdf",396041,1,12,"English","en",105,"# Introduction\n## Customer churn as a telecom challenge\n## Limitations of traditional reactive measures\n## Role of advanced machine learning\n## Research objective and dataset overview\n## Feature set and modeling goal","[{\"question\":\"What problem does the paper address in the telecommunications industry?\",\"answer\":\"The paper addresses customer churn, which harms revenue, brand reputation, and market share for telecom providers.\"},{\"question\":\"Which machine learning models are compared for churn prediction?\",\"answer\":\"The study compares Random Forest, CatBoost, and K-Nearest Neighbors (KNN) for predicting churn performance.\"},{\"question\":\"What performance does Random Forest achieve in the study?\",\"answer\":\"Random Forest achieves 99% accuracy and 99% F1 score, with an 88% recall rate and an AUC of 0.99.\"}]","Leveraging Machine Learning Models for Customer Churn Prediction in Telecommunications - 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