Makine Öǧrenmesi Teknikleri Ile İnternet Servis Saǧlayıcısı için Müşteri Kayıp Tahmini

dc.contributor.author Göy, Gökhan
dc.contributor.author Kolukisa, Burak
dc.contributor.author Bahçevan, Cenk Anıl
dc.contributor.author Güngör, Vehbi Çağrı
dc.date.accessioned 2025-09-25T10:37:16Z
dc.date.available 2025-09-25T10:37:16Z
dc.date.issued 2020
dc.description.abstract With the developing technology in every fields, a competitive marketing environment has been arised. In this competitive environment, analyzing customer behavior has become vital. In particular, the ability to easily change any service provider has become very critical for the company to continue its existence. At the same time, the amount of financial resources spent on retaining customers much less than to obtain new clients. In this context, the traditional methods of examining vast amount of data obtained today for establishing decision support systems have lost their validities. In this study, we used a dataset which is provided by TurkNet serving as an internet service provider in Turkey. Various preprocessing steps has performed on this dataset and then classification algorithms ran. Afterwards results have obtained and compared. The results of these experiments analyzed in terms of the area under the curve value. In this context, the most successful classifier algorithm has been determined as the Random Trees algorithm with a value of 0.936. © 2020 Elsevier B.V., All rights reserved. en_US
dc.identifier.doi 10.1109/UBMK50275.2020.9219369
dc.identifier.isbn 9781728175652
dc.identifier.scopus 2-s2.0-85095707621
dc.identifier.uri https://doi.org/10.1109/UBMK50275.2020.9219369
dc.identifier.uri https://hdl.handle.net/20.500.12573/2945
dc.language.iso tr en_US
dc.publisher Institute of Electrical and Electronics Engineers Inc. en_US
dc.relation.ispartof -- 5th International Conference on Computer Science and Engineering, UBMK 2020 -- Diyarbakir -- 164014 en_US
dc.rights info:eu-repo/semantics/closedAccess en_US
dc.subject Binary Classification en_US
dc.subject Churn Prediction en_US
dc.subject Data Mining en_US
dc.subject Machine Learning en_US
dc.subject Classification (Of Information) en_US
dc.subject Decision Support Systems en_US
dc.subject Machine Learning en_US
dc.subject Predictive Analytics en_US
dc.subject Trees (Mathematics) en_US
dc.subject Web Services en_US
dc.subject Area Under The Curves en_US
dc.subject Classification Algorithm en_US
dc.subject Classifier Algorithms en_US
dc.subject Competitive Environment en_US
dc.subject Customer Behavior en_US
dc.subject Financial Resources en_US
dc.subject Machine Learning Techniques en_US
dc.subject Pre-Processing Step en_US
dc.subject Internet Service Providers en_US
dc.title Makine Öǧrenmesi Teknikleri Ile İnternet Servis Saǧlayıcısı için Müşteri Kayıp Tahmini en_US
dc.title.alternative Ensemble Churn Prediction for Internet Service Provider with Machine Learning Techniques en_US
dc.type Conference Object en_US
dspace.entity.type Publication
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gdc.description.department Abdullah Gül University en_US
gdc.description.departmenttemp [Göy] Gökhan, Mühendislik Fakültesi Bilgisayar Mühendisliǧi, Abdullah Gül Üniversitesi, Kayseri, Turkey; [Kolukisa] Burak, Mühendislik Fakültesi Bilgisayar Mühendisliǧi, Abdullah Gül Üniversitesi, Kayseri, Turkey; [Bahçevan] Cenk Anıl, TurkNet İletişim Hizmetleri, Istanbul, Turkey; [Güngör] Vehbi Çağrı, Mühendislik Fakültesi Bilgisayar Mühendisliǧi, Abdullah Gül Üniversitesi, Kayseri, Turkey en_US
gdc.description.endpage 253 en_US
gdc.description.publicationcategory Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı en_US
gdc.description.scopusquality N/A
gdc.description.startpage 248 en_US
gdc.description.wosquality N/A
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