Analysis of customer churn prediction using machine learning and deep learning algorithms

Abstract

The telecommunication industry need a customer churn prediction due to many competitors. The companies also lack of churn prediction to retain the customer. This problem not only affect the growth of the business but also affect the revenues.  To retain the existing customer is very crucial task for the company. A rapid increasing in technology, the various machine learning and deep learning are tools are developed which can be used by telecoms companies to monitor the churn behaviour of customers. In this study, a brief idea on the customer churn problem on various machine learning techniques such as XGBoost, Gradient Boost, AdaBoost, ANN, Logistic Regression and Random Forest are analysed.  Also the various deep learning techniques such as Convolutional Neural Network, stacked auto encoders to predict the customer churn problem are analysed by comparing the models in terms of accuracy.