Summary: Customer churn is a business term used to describe customer churn. It describes customers who leave or switch to competitors. In the telecommunications industry, customers have multiple service choices and frequently switch from one service to another. In this competitive market, customers demand the best products and services at low prices, while service providers constantly focus on achieving their business goals. This is why customer churn is very high in the telecommunications industry, with an average annual churn rate of 30-35%. The aim of this paper is to propose an effective customer churn prediction model, based on classification techniques, which will help the telecom company to predict the customer churn rate in order to know which customers are loyal to them.I . INTRODUCTIONData mining is a very well-known technique for churn prediction and it is used in many fields. Data mining refers to the process of analyzing data to determine patterns and their relationships. This is an advanced technique that digs deeper into the data and uses machine learning algorithms to automatically scan through each record and variable to uncover patterns and information that may have been hidden. There is a lot of work being done in data mining for churn prediction in different fields. It is used to solve the problem of customer churn by identifying customer behavior from a large amount of customer data. Problem Statement: Customer churn refers to the periodic loss of customers in an organization. Customer churn is a very common problem in all organizations around the world. In a competitive market, it is a very big challenge for any organization to maintain...... middle of paper ......iningMart: Proceedings of the Workshop on Data Mining and…, 2005 – Citeseer[16] Mozer MC, Wolniewicz R., Grimes DB, Johnson E., Kaushansky H. Predicting subscriber dissatisfaction and improving retention in the wireless telecommunications industry. IEEE Transactions on Neural Networks, Special Issue on Data Mining and Knowledge Representation (2000).[17] Mutanen, Teemu. Customer Churn Analysis – A Case Study, Research Report VTTR0118406, March 15, 2006.[18] De Oliveira, JV, Pedrycz W. (editors) (2007) Advances in Fuzzy Clustering and its Applications, John Wiley & Sons Ltd.[19] J. Hadden, A. Tiwari, R. Roy and D. Ruta. Predicting churn using complaints data. International Journal of Intelligent Technology, 13:158{163, May 2006.[20] H. Van Khuu, H.-KieLee and J.-Liang Tsai. “Machine learning with neural networks and support vector machines”, 2005.