Analysis of Customer Churn Prediction in Telecom Sector Using CART Algorithm Sandeep Rai, Nikita Khandelwal and Rajesh Boghey Abstract Predicting client churn in telecommunication industries becomes the most significant topic for analysis in recent years. Because its helps in detecting which customer are likely to change or cancel their subscription to a service. Analysis of information that is extracted from telecommunication companies will help to seek out the explanations of client churn and also uses the knowledge to retain the purchasers. Thus, predicting churn is extremely necessary for telecommunication firms to retain their customers. During this paper, we have designed the classification model using call tree, evaluated the performance measures, and compared its performance with logistic regression model. Keywords Classification · Churn prediction · Telecom data · Logistic regression model · Customer retention · CART algorithm 1 Introduction Data mining strategies lie at the intersection of computing, statistics, and machine learning info systems. Data processing techniques help in building the prediction models to get future developments and actions permitting the organizations to require good selections derived from the data from knowledge [1]. Churn prediction is associate application of client performance in data processing. Churn [2] could be a key issue sweet-faced through associate enterprise associated denoted the value of extending a replacement client is almost five times more than the value of maintaining an recent client. As a result of the fight of the enterprise market S. Rai (B ) · N. Khandelwal · R. Boghey Department of Computer Science and Engineering, Technocrats Institute of Technology (Excellence), Bhopal, India e-mail: sandtec@gmail.com N. Khandelwal e-mail: nikitakhandelwal0000@gmail.com R. Boghey e-mail: rajeshboghey@gmail.com © Springer Nature Singapore Pte Ltd. 2020 A. K. Luhach et al. (eds.), First International Conference on Sustainable Technologies for Computational Intelligence, Advances in Intelligent Systems and Computing 1045, https://doi.org/10.1007/978-981-15-0029-9_36 457