Customer Churn Prediction For Telecommunication Industry

Abstract

Customer churn prediction for telecommunication industry Thesis I used 2 models from dataset KAHHLE to do the work. .K- nearest neighbor algorithm, Decision tree Algorithm Two Datasets are working well but I think K Neighbors is giving good results this system is working well so this dataset I think this dataset will work well. With the rapid development of Telecommunication Industry, the service providers are inclined more towards expansion of the subscriber base. To meet the need of surviving in the competitive environment, the retention of existing customers has become a huge challenge. In the survey done in the Telecom industry, it is stated that the cost of acquiring a new customer is far more that retaining the existing one. Therefore, by collecting knowledge from the telecom industries can help in predicting the association of the customers as whether or not they will leave the company. The required action needs to be undertaken by the telecom industries in order to initiate the acquisition of their associated customers for making their market value stagnant. Our paper proposes a new framework for the churn prediction model and implements it using the WEKA Data Mining software. The efficiency and the performance of Decision tree and Logistic regression techniques have been compared.