Testing the performance of feature selection methods for customer churn analysis: Case study in B2B business

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dc.authorid0000-0002-4958-4575en_US
dc.authorscopusid55355863500en_US
dc.authorwosidGHQ-7349-2022en_US
dc.contributor.authorSancar, Semanur
dc.contributor.authorUzun-Per, Meryem
dc.contributor.editorGarcía Márquez, Fausto Pedro
dc.contributor.editorJamil, Akhtar
dc.contributor.editorEken, Süleyman
dc.contributor.editorHameed, Alaa Ali
dc.date.accessioned2023-04-11T11:21:12Z
dc.date.available2023-04-11T11:21:12Z
dc.date.issued2023en_US
dc.departmentFakülteler, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.description.abstractChurn analysis has recently become one of the favorite topics of marketing teams with the development of machine learning models. This study aims to discover the most suitable feature selection (FS) model for churn analysis by using the databases of BiletBank, a business-to-business (B2B) company. It was found that some categorical data such as agency type and currency used by customers, along with periodic flight sales data, are also meaningful features for churn analysis in the BiletBank customer portfolio. This feature selection study in the database will be a source for future churn analysis studies.en_US
dc.identifier.citationSancar, S., & Uzun-Per, M. (2023). Testing the performance of feature selection methods for customer churn analysis: Case study in B2B business. F.P. García Márquez, A. Jamil, S. Eken, A.A. Hameed, (Eds.), International Conference on Computing, Intelligence and Data Analytics (pp 509-519). Springer: Cham. https://doi.org/10.1007/978-3-031-27099-4_39en_US
dc.identifier.doi10.1007/978-3-031-27099-4_39en_US
dc.identifier.endpage519en_US
dc.identifier.isbn9783031270994
dc.identifier.isbn9783031270987
dc.identifier.scopus2-s2.0-85151063013en_US
dc.identifier.scopusqualityQ4en_US
dc.identifier.startpage509en_US
dc.identifier.urihttps://doi.org/10.1007/978-3-031-27099-4_39
dc.identifier.urihttps://hdl.handle.net/20.500.13055/442
dc.indekslendigikaynakScopusen_US
dc.institutionauthorUzun-Per, Meryem
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofInternational Conference on Computing, Intelligence and Data Analyticsen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectCustomer Churn Analysisen_US
dc.subjectFeature Selectionen_US
dc.subjectB2Ben_US
dc.subjectSequential Forward Selectionen_US
dc.subjectSequential Backward Selectionen_US
dc.subjectClassificationen_US
dc.subjectLogistic Regressionen_US
dc.subjectSupport Vector Machinesen_US
dc.subjectRandom Forest Classifieren_US
dc.subjectExtra Tress Classifieren_US
dc.titleTesting the performance of feature selection methods for customer churn analysis: Case study in B2B businessen_US
dc.typeConference Objecten_US
dspace.entity.typePublication

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