Abstract: Predicting customer churn in telecommunication industries becomes a most important topic for research in recent years. Because its helps in detecting which customer are likely to change or cancel their subscription to a service. Now a days the mobile telecom market has growing market rapidly and all the telecommunication industries focused on building a large customer base into keeping customers in house. So it is very important to find which customers are wants to switch to a other competitor by cancel their subscription in the near future. Analysis of data which is extracted from telecom companies can helps to find the reasons of customer churn and also uses the information to retain the customers.  In order to retain existing customers, Telecom providers need to know the reasons of churn, which can be realized through the knowledge extracted from Telecom data. In this we can focuses on machine learning techniques for predicting customer churn through which we can build the classification models such as logistic Regression, Random Forest and Gradient Boosting Algorithm and also compare the performance of these models.

Keywords: Churn prediction, data mining, telecom system , Customer retention, classification system.

PDF | DOI: 10.17148/IJARCCE.2023.12744

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