Customer Churn Scrutiny and Prediction Using Data Extraction Models in Funding Sectors

P. Ramalingamma; G. Subba Rao; K. Hari; T. R. Chaitanya

doi:10.55524/ijircst.2022.10.2.113

Abstract

A new method for customer churn analysis and prediction has been proposed. The method uses data Extraction model in Funding industries. This has been inspired by the fact that there are around 1,5 million churn customers in a year which is increasing every year. Churn customer prediction is an activity carried out to predict whether the customer will leave the company or not. One way to predict this customer churn is to use a catalog technique from data Extraction that produces a machine learning model. This study tested 5 different catalog methods with a dataset consisting of 57 attributes. Experiments were carried out several times using comparisons between different classes. Support Vector Machine (SVM) with a comparison of 50:50 Class sampling data is the best method for predicting churn customers at a private bank in Indonesia. The results of this modeling can be utilized by company who will apply strategic action to prevent customer churn.

Keywords

Customer churn, prediction, data Extraction, catalog, machine learning.

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Cites this article as

[P. Ramalingamma, G. Subba Rao, K. Hari, T. R. Chaitanya (2022), Customer Churn Scrutiny and Prediction Using Data Extraction Models in Funding Sectors, International Journal of Innovative Research in Computer Science and Technology (IJIRCST), Vol-10, Issue-2, Page No-599-605], (ISSN 2347 - 5552). www.ijircst.org

Corresponding Author

P. Ramalingamma

Assistant Professor, Department of Information Technology, PACE Institute of Technology and Sciences, Ongole, Andhra Pradesh, India

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