Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Conference Papers Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Comparative Analysis of Predictive Models for Customer Churn Prediction in the Telecommunication Industry Subject The topic of the resource Attrition; customer segmentation; retention Description An account of the resource To determine the best model for churn prediction in the telecom industry, this paper compares 11 machine learning algorithms namely Logistic Regression, Support Vector Machine, Random Forest, Decision Tree, XGBoost, LightGBM, Cat Boost, AdaBoost, Extra Trees, Deep Neural Network, and Hybrid Model (MLPClassifier). It also aims to pinpoint the top three factors that lead to customer churn and conducts customer segmentation to identify vulnerable groups. The results indicate that the Logistic Regression model performs the best, with an F1 score of 0.6215, 81.76% accuracy, 68.95% precision, and 56.57% recall. The top three attributes that cause churn are found to be tenure, Internet Service Fiber optic, and Internet Service DSL; conversely, the top three models in this article that perform the best are Logistic Regression, Deep Neural Network, and AdaBoost. The K means algorithm is applied to establish and analyze four different customer clusters. This study has effectively identified customers that are at risk of churn and may be utilized to develop and execute strategies that lower customer attrition. 2024 IEEE. Creator An entity primarily responsible for making the resource Christopher D.; Anand G. Source A related resource from which the described resource is derived Proceedings of International Conference on Communication, Computer Sciences and Engineering, IC3SE 2024, pp. 534-539. Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Date A point or period of time associated with an event in the lifecycle of the resource 2024-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/IC3SE62002.2024.10592931" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/IC3SE62002.2024.10592931</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85200687706&amp;doi=10.1109%2FIC3SE62002.2024.10592931&amp;partnerID=40&amp;md5=dfd6a63d86af24a50fb2ab032e8d8a83" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85200687706&amp;doi=10.1109%2fIC3SE62002.2024.10592931&amp;partnerID=40&amp;md5=dfd6a63d86af24a50fb2ab032e8d8a83</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 979-835036684-6 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Conference paper Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Christopher D., Christ (Deemed To Be University), Delhi NCR, India; Anand G., Christ (Deemed To Be University), Delhi NCR, India