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has been cited by the following article:

• TITLE: Churn Prediction Using Machine Learning and Recommendations Plans for Telecoms

  AUTHORS: Khulood Ebrah, Selma Elnasir

  KEYWORDS: Churn Prediction, Telecommunication, Modeling, Analysis, SVM, Naïve Bayes, Decision Trees, Cell2cell, IBM

  JOURNAL NAME: Journal of Computer and Communications, Vol.7 No.11, November 5, 2019

  ABSTRACT: Keeping customers satisfied is truly essential for saying that business is successful especially in the telecom. Many companies experience different techniques that can predict churn rates and help in designing effective plans for customer retention since the cost of acquiring a new customer is much higher than the cost of retaining the existing one. In this paper, three machine learning algorithms have been used to predict churn namely, Na?ve Bayes, SVM and decision trees using two benchmark datasets IBM Watson dataset, which consist of 7033 observations, 21 attributes and cell2cell dataset that contains 71,047 observations and 57 attributes. The models’ performance has been measured by the area under the curve (AUC) and they scored 0.82, 0.87, 0.77 respectively for IBM dataset and 0.98, 0.99, 0.98 respectively for cell2cell dataset. The proposed models also obtained better accuracy than the previous studies using the same datasets.