Increase in the number of network providers and their competition with each other in the market should achieve retention of customers which is very important for their growth and survival. Customer churn happens when subscribers stop doing business with a company or service. Customer churn is also known as customer attrition. Several data mining techniques have been proposed to analyze customer churn and they all give some results but which is better must be known. To some extent it is possible to predict the customer churn rate.This study includes the techniques such as the Logistic Regression, Decision Tree and the k-means clustering and we see that the accuracy given by the Logistic regression is better than other.
|Number of pages||7|
|Journal||Journal of Advanced Research in Dynamical and Control Systems|
|Issue number||4 Special Issue|
|Publication status||Published - 01-01-2019|
All Science Journal Classification (ASJC) codes
- Computer Science(all)