Predictive Framework for Advanced Customer Churn Prediction using Machine Learning

Abstract

In recent years, the telecom sector has been burgeoning to satisfy the demand of mobile subscribers and telecom service providers.The increase in number of mobile subscribers and competition among providers, results in the creation of "churners".These are the subscribers who tend to switch from the current telecom service to another.The detection of these churners is called "churn prediction".This prediction has become a major challenge for telecom companies.The main purpose of customer churn prediction is to estimate the number of subscribers those who want to quit the current service provider by providing specific solutions to retain them.This paper proposes methods for the estimation of churners by applying different classification techniques and estimates the differences between them.The performance is measured by taking different parameters like accuracy, precision, recall, etc.In this paper, the various performance measurement and comparison are done by using the dataset collected from American Telecommunication Company.All the proposed work is based on Machine Learning, inculcating the supervised learning.In addition to all, a single test-bed is designed as a user interface to predict the individual customer according to different attributes.