Predicting customer churn in telecommunication sector using Naïve Bayes algorithm

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The telecom sector creates huge amounts of information every day as a result of its large customer base. Business professionals and decision-makers emphasized that maintaining existing clients is less expensive than recruiting new ones. Business analysts and customer relationship management (CRM)need to know the reasons why customers leave and the behavior patterns from earlier churn consumer’s data. Today, there is a problem with customer churn examination and prediction in the telecom industry since it is crucial for the sector to examine customer behavior to identify those who are going to stop their subscriptions. Customer retention could be increased by utilizing detection system to detect consumer behavior. Recent advancements in machine learning(ML)have made churn prediction more precise and practical. It is essential for identifying customers ready to leave using company’s products and services in the early stage. Hence in this work, predicting customers churn in telecommunication sector usingNaïveBayes(NB) model is presented. The performance of presentedNBalgorithmis evaluated using the parameters accuracy, precision, and sensitivity. The NB algorithm will have better performance than pervious approaches.