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Introduction Most telecommunication companies consider the client as the most significant advantage for them. For that reason, nowadays, a difficult problem that encounters telecommunication firms is when the client leaves the company to some other service supplier for a reason or another . Typically, this churn can occur in rates which seriously impact the profitability of the companies as it's simple for the customers to switch companies. In market, where the rivalry between the telecommunication firms grows rapidly, businesses have shifted their focus from acquiring new customers to keep their current ones [1--3]. Fundamentally, churn is one of those significant problems and companies began to seek new Business Intelligence (BI) applications that predict churn customers. When the company is aware of the percentage of consumers who leave for another company in a given time period, it wouldbe easier to think of a detailed analysis of the causesfor the churn rate and understand the behavior of customersthat unsubscribe and move to other business competitor. Thishelps in preparation effective customer retention strategies for that firm ]. Among many approaches developed in the literature for predicting customer churn, supervised Machine Learning (ML) techniques are the most frequently investigated [5--9]. Supervised ML concerns the developing of models whichcan learn from labeled data. ML comprises a wide rangeof algorithms like Decision trees, k-nearest neighbors,Linear regression, Naive Bayes, Neural networks, Supportvector machines (SVM), Genetic Programming and several more. As an instance, in  authors conducted a comparative analysis of linear regression and two machine learning techniques; neural netwo...