IJIRCST

Analysis of Customer Churn Prediction in Telecom Industry Using Logistic Regression

K. Sandhya Rani | Shaik Thaslima | N.G.L. Prasanna | R.Vindhya | P. Srilakshmi

Customers plays an import role in industry to run industry. Churn of the customer may lead many consequences. Customer churn prediction must the important aspect of any company. This helps in the detection of customers who are likely to cancel a subscription to a service. Recently, the mobile telecommunication market has changed from a rapidly growing market into a state of saturation The focus of telecommunication companies is to shift from growing of large customer into keeping customers in house. For that reason, it is valuable to know which customers are likely to switch to a competitor in future. the model is proposed for churn prediction for telecommunication companies using machine learning techniques namely logistic regression. A comparison is done on the efficiency of the algorithm on the available dataset.

Machine learning, logistic regression, variance reduction, Bayesian Models, CRM (Customer Relationship Management)

A Hybrid Churn Prediction Model in Mobile Telecommunication Industry ,Georges D. Olle Olle and Shuqin Cai ,International Journal of e-Education, e-Business, e-Management and e-Learning, Vol. 4, No. 1, February 2014.
Telecommunication Subscribers' Churn Prediction Model Using Machine Learning, Saad Ahmed Qureshi, Ammar Saleem Rehman, Ali Mustafa Qamar, Aatif Kamal, Ahsan Rehman, IEEE International Conference on Digital Information Management (ICDIM), 2013 Eighth International Conference on, 2013, pp. 131–136.
Customer Churn Prediction in Telecommunication Industries using Data Mining Techniques- A Review, Kiran Dahiya and Kanika Talwar, International Journal of Advanced Research in Computer Science and Software Engineering, Volume 5, Issue 4, 2015.
Churn Prediction in Telecommunication Using Classification Techniques Based on Data Mining: A Survey, Nisha Saini and Monika, International Journal of Advanced Research in Computer Science and Software Engineering, Volume 5, Issue 3, March 2015.
Churn Prediction In Mobile Telecom System Using Data Mining Techniques, Dr. M. Balasubramaniam, M.Selvarani, International Journal of Scientific and Research Publications, Volume 4, Issue 4, April 2014.
Predicting Customer Churn in Mobile Telephony Industry Using Probabilistic Classifiers in Data Mining, Clement Kirui1, Li Hong, Wilson Cheruiyot and Hillary Kirui, IJCSI International Journal of Computer Science Issues, Vol. 10, Issue 2, No 1, March 2013.
Applying Data Mining Techniques in Telecom Churn Prediction, N.Kamalraj and Dr.A.Malathi, International Journal of Advanced Research in Computer Science and Software Engineering, Volume 3, Issue 10, October 2013.
Churn Prediction in Telecommunication Using Data Mining Technology, Rahul J. Jadhav and Usharani T. Pawar, (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 2, No.2, February 2011.
Analysis of Customer Churn in Mobile Industry using Data Mining, Aishwarya Churi, Mayuri Divekar, Sonal Dashpute, Prajakta Kamble, International Journal of Emerging Technology and Advanced Engineering, Volume 5, Issue 3, March 2015.
A Proposed Churn Prediction Model, Essam Shaaban, Yehia Helmy, Ayman Khedr, Mona Nasr / International Journal of Engineering Research and Applications (IJERA) ,Vol. 2, Issue 4, June-July 2012.

[K. Sandhya Rani, Shaik Thaslima, N.G.L. Prasanna, R.Vindhya, P. Srilakshmi (2021), Analysis of Customer Churn Prediction in Telecom Industry Using Logistic Regression, International Journal of Innovative Research in Computer Science & Technology (IJIRCST), Vol-9, Issue-4, Page No-27-29], (ISSN 2347 - 5552). www.ijircst.org

K. Sandhya Rani

Assistant Professor, Department of Computer Science and Engineering, Dhanekula Institute of Engineering and Technology, Vijayawada, Andhra Pradesh, India

DOI:

DOI: 10.21276/ijircst.2021.9.4.6 DOI URL: https://doi.org/10.21276/ijircst.2021.9.4.6

Copyright © 2021 by author(s) and International Journal of Innovative Research in Computer Science & Technology Journal. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0) (http://creativecommons.org/licenses/by/4.0)