Customer Churn Prediction Using Machine Learning Methods: A Comparative Analysis

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dc.authorscopusid57195222623
dc.authorscopusid15077642900
dc.authorscopusid26421178600
dc.contributor.authorKaramollaoğlu, Hamdullah
dc.contributor.authorYücedağ, İbrahim
dc.contributor.authorDoğru, İbrahim Alper
dc.date.accessioned2023-07-26T11:55:17Z
dc.date.available2023-07-26T11:55:17Z
dc.date.issued2021
dc.departmentDÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.description6th International Conference on Computer Science and Engineering, UBMK 2021 -- 15 September 2021 through 17 September 2021 -- 176826en_US
dc.description.abstractCustomer churn analysis is the process of predicting customers who tend to cancel the service (subscription) they receive for various reasons, especially in sectors such as telecommunications, finance and insurance, and determining the necessary operational steps to prevent this cancellation. The study used two separate datasets from kaggle.com to identify customers who tend to unsubscribe in the telecommunications industry. The analysis process was carried out by applying machine learning methods such as Logistic Regression, K-Nearest Neighbor, Decision Trees, Random Forest, Support Vector Machines, AdaBoost, Multi-Layer Sensors and Naive Bayes methods on the relevant datasets. It was seen that the most successful method in the customer loss analysis performed on both datasets was the Random Forest method. © 2021 IEEEen_US
dc.identifier.doi10.1109/UBMK52708.2021.9558876
dc.identifier.endpage144en_US
dc.identifier.isbn9.78167E+12
dc.identifier.scopus2-s2.0-85125880318en_US
dc.identifier.startpage139en_US
dc.identifier.urihttps://doi.org/10.1109/UBMK52708.2021.9558876
dc.identifier.urihttps://hdl.handle.net/20.500.12684/13041
dc.indekslendigikaynakScopusen_US
dc.institutionauthorKaramollaoğlu, Hamdullah
dc.institutionauthorYücedağ, İbrahim
dc.language.isotren_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartofProceedings - 6th International Conference on Computer Science and Engineering, UBMK 2021en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmz$2023V1Guncelleme$en_US
dc.subjectCustomer churn analysisen_US
dc.subjectMachine learningen_US
dc.subjectTelecommunicationen_US
dc.subjectAdaptive boostingen_US
dc.subjectDecision treesen_US
dc.subjectLogistic regressionen_US
dc.subjectNearest neighbor searchen_US
dc.subjectRandom forestsen_US
dc.subjectSupport vector machinesen_US
dc.subjectTelecommunication industryen_US
dc.subjectAnalysis processen_US
dc.subjectChurn analysisen_US
dc.subjectComparative analyzesen_US
dc.subjectCustomer churn analyseen_US
dc.subjectCustomer churn predictionen_US
dc.subjectCustomer churnsen_US
dc.subjectLogistics regressionsen_US
dc.subjectMachine learning methodsen_US
dc.subjectNearest-neighbouren_US
dc.subjectTelecommunications industryen_US
dc.subjectSalesen_US
dc.titleCustomer Churn Prediction Using Machine Learning Methods: A Comparative Analysisen_US
dc.title.alternativeMakine Öğrenmesi Yöntemleri Kullanılarak Müşteri Kaybı Tahmini: Karşılaştırmalı Bir Analizen_US
dc.typeConference Objecten_US

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