Customer churn prediction in telecom sector using machine learning techniques

Abstract

In the telecom industry, large-scale of data is generated on daily basis by an enormous amount of customer base. Here, getting a new customer base is costlier than holding the current customers where churn is the process of customers switching from one firm to another in a given stipulated time. Telecom management and analysts are finding the explanations behind customers leaving subscriptions and behavior activities of the holding churn customers’ data. This system uses classification techniques to find out the leave subscriptions and collects the reasons behind the leave subscription of customers in the telecom industry. The major goal of this system is to analyze the diversified machine learning algorithms which are required to develop customer churn prediction models and identify churn reasons in order to give them with retention strategies and plans. In this system, leave subscriptions collects customers' data by applying classification algorithms such as Random Forest (RF), machine learning techniques such as KNN and decision tree Classifier. It offers an efficient business model that analyzes customer churn data and gives accurate predictions of churn customers so that business management may take action within the churn period to stop churn as well as loss in profit. System achieves an accuracy of 99 % using the random forest classifier for churn predicts, the classifier matrix has achieved a precision of 99 % with a recall factor of 99 % alongwith received overall accuracy of 99.09 %. Likewise, our research work improves churn prediction, scope other business fields, and provide prediction models to hold their existing customers customer service, and avoid churn effectively.

Keywords

• Churners
• Customer churn prediction
• Up-sampling
• Classifiers
• Survival analysis