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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