Yayın: New approach for predictive churn analysis in Telecom
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In this article, we propose a new approach for the churn analysis. Our target sector is Telecom industry, because most of the companies in the sector want to know which of the customers want to cancel the contract in the near future. Thus, they can propose new offers to the customers to convince them to continue using services from same company. For this purpose, churn analysis is getting more important. We analyze well-known machine learning methods that are logistic regression, Naïve Bayes, support vector machines, artificial neural networks and propose new prediction method. Our analysis consist of two parts which are success of predictions and speed measurements. Affect of the dimension reduction is also measured for the analysis. In addition, we test our new method with a second dataset. Artificial neural networks is the most successful as we expected but our new approach is better than artificial neural networks when we try it with data set 2. For both data sets, new method gives the better result than logistic regression and Naïve Bayes.
Yapay Sinir Ağları, Kayıp Analizi, Lojistik Regresyon, Destek Vektörü, Artificial Neural Networks, Churn Analysis, Logistic Regression, Support Vector, Naïve Bayes