Makine Öǧrenmesi Teknikleri Ile İnternet Servis Saǧlayıcısı için Müşteri Kayıp Tahmini - Göy, Gökhan; Kolukisa, Burak; Bahçevan, Cenk Anıl; Güngör, Vehbi Çağrı (Institute of Electrical and Electronics Engineers Inc., 2020)
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.
Ensemble Churn Prediction for Internet Service Provider with Machine Learning Techniques
Keywords: Binary Classification; Churn Prediction; Data Mining; Machine Learning; Classification (Of Information); Decision Support Systems; Machine Learning; Predictive Analytics; Trees (Mathematics); Web Services; Area Under The Curves; Classification Algorithm; Classifier Algorithms; Competitive Environment; Customer Behavior; Financial Resources; Machine Learning Techniques; Pre-Processing Step; Internet Service Providers