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AKILLI SİSTEMLER VE UYGULAMALARI DERGİSİ
JOURNAL OF INTELLIGENT SYSTEMS WITH APPLICATIONS
J. Intell. Syst. Appl.
E-ISSN: 2667-6893
Creative Commons License This work is licensed under a Creative Commons Attribution 4.0 International License.

The Implementation of Classification and Clustering Techniques on Churn Analysis

Churn Analizinde Sınıflandırma ve Kümeleme Tekniklerinin Uygulanması

How to cite: Elbir A, İlhan HO, Aydın MF, Demirbulut YE. The implementation of classification and clustering techniques on churn analysis. Akıllı Sistemler ve Uygulamaları Dergisi (Journal of Intelligent Systems with Applications) 2019; 2(1): 72-75. DOI: 10.54856/jiswa.201905065

Full Text: PDF, in Turkish.

Total number of downloads: 702

Title: The Implementation of Classification and Clustering Techniques on Churn Analysis

Abstract: One of the most important problems of telecommunication companies is the potential transfer of customers between the firms. In order to avoid this problem, it is very important to identify customers who are likely to leave. In this study, the performance of the classification and the clustering algorithms in machine learning techniques has been evaluated and compared on the analysis of potential customer trends, which have been reported as churn analysis. K nearest neighbors, decision trees, random forests, support vector machines and naïve bayes methods were tested in scope of classification idea. Additionally, K-Means and hierarchical clustering methods were tested. The performances of the methods have been evaluated according to the accuracy, precision, sensitivity and F-measure performance metrics.

Keywords: Churn analysis; machine learning; classification; clustering


Başlık: Churn Analizinde Sınıflandırma ve Kümeleme Tekniklerinin Uygulanması

Özet: Telekomünikasyon firmalarının en büyük sorunlarından biri, firmalar arası potansiyel müşteri transferleridir. Bu sorunun önüne geçmek amacıyla ayrılma ihtimali olan müşterilerin önceden tespit edilmesi büyük önem taşır. Yapılan çalışmada, churn analizi olarak belirtilen potansiyel müşteri ayrılma eğilimlerinin analizleri üzerinde makine öğrenmesi tekniklerinden sınıflandırma ve kümeleme algoritmalarının başarımları ölçülmüş ve karşılaştırılması yapılmıştır. Sınıflandırma tekniklerinden K en yakın komşular, karar ağaçları, rastgele ormanlar, Destek Vektör Makineleri ve Naïve Bayes yöntemleri, kümeleme yöntemlerinden ise K-ortalama, Hiyerarşik kümeleme yöntemleri uygulanmıştır. Yöntemlerin başarımları hata oranı, kesinlik, duyarlılık ve F-ölçütü performans ölçütlerine göre değerlendirilmiştir.

Anahtar kelimeler: Churn analizi; makine öğrenmesi; sınıflama; kümeleme


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