An Optimal Churn Prediction Model using Support Vector Machine with Adaboost

Authors(2) :-A. Saran Kumar, Dr. D. Chandrakala

Customer churn is a common measure of lost customers. By minimizing churn, a company can maximize its profits. Companies have recognized that existing customers are most valuable assets. Customer retention is important for a good marketing and a customer relationship management strategy. In this paper, a detailed scheme is worked out to convert raw customer data into meaningful and useful data that suits modelling buying behaviour and in turn to convert this meaningful data into knowledge for which predictive data mining techniques are adopted. In this work, a boosted version of SVM which is a combination of SVM with Adaboost is used for increasing the accuracy of generated rules. Boosted versions have high accuracy and performance than non-boosted versions. The aim of churn prediction model is to detect the customers with high tendency to leave the firm and also increase the revenue for the firm.

Authors and Affiliations

A. Saran Kumar
Kumaraguru College of Technology, Coimbatore, Tamil Nadu, India
Dr. D. Chandrakala
Kumaraguru College of Technology, Coimbatore, Tamil Nadu, India

Churn, Adaboost, SVM, Classification and Prediction.

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Publication Details

Published in : Volume 2 | Issue 1 | January-February 2017
Date of Publication : 2017-02-28
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 225-230
Manuscript Number : CSEIT172155
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

A. Saran Kumar, Dr. D. Chandrakala, "An Optimal Churn Prediction Model using Support Vector Machine with Adaboost", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 2, Issue 1, pp.225-230, January-February-2017.
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