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 naive 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.