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A Framework to Enhance Accuracy of Customer Churn Prediction in Telecom Industry

by Kholoud T. Mahmoud, Shimaa Ouf, Manal A. Abdel-Fattah
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 184 - Number 27
Year of Publication: 2022
Authors: Kholoud T. Mahmoud, Shimaa Ouf, Manal A. Abdel-Fattah
10.5120/ijca2022922342

Kholoud T. Mahmoud, Shimaa Ouf, Manal A. Abdel-Fattah . A Framework to Enhance Accuracy of Customer Churn Prediction in Telecom Industry. International Journal of Computer Applications. 184, 27 ( Sep 2022), 50-56. DOI=10.5120/ijca2022922342

@article{ 10.5120/ijca2022922342,
author = { Kholoud T. Mahmoud, Shimaa Ouf, Manal A. Abdel-Fattah },
title = { A Framework to Enhance Accuracy of Customer Churn Prediction in Telecom Industry },
journal = { International Journal of Computer Applications },
issue_date = { Sep 2022 },
volume = { 184 },
number = { 27 },
month = { Sep },
year = { 2022 },
issn = { 0975-8887 },
pages = { 50-56 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume184/number27/32488-2022922342/ },
doi = { 10.5120/ijca2022922342 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:24:52.975270+05:30
%A Kholoud T. Mahmoud
%A Shimaa Ouf
%A Manal A. Abdel-Fattah
%T A Framework to Enhance Accuracy of Customer Churn Prediction in Telecom Industry
%J International Journal of Computer Applications
%@ 0975-8887
%V 184
%N 27
%P 50-56
%D 2022
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Customer Churn Prediction problem is a long-standing challenge for Different communities, there are many groups in the scientific and commercial communities like telecom sector trying to improve Predictions. The primary motivation is the dire need of businesses to retain existing customers, coupled with the high cost associated with acquiring new one. The machine learning techniques have a significant impact onimproving and predicting customer data mining techniques to improve customer retention, but thesetechniques face a lot of challenges in terms of accuracy. This study aimed to enhance prediction and detection using a comparative study on the most popular supervised machinelearning methods , Support Vector Machine (SVM) andextreme Gradient Boosting (XGBoost) model to detectcustomer churn in IBM Watson dataset of telecom company. This paper provides XG boost classifier which less focused in the previousworks. XG boost classifier is applied on publicly available telecom dataset and experiential results are compared with SVM Classifier. XG boost classifier performs superior out of SVM.The evaluated metrics such as Precision, Recall, F1-score. It yielded an accuracy of the framework reached 84%.

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Index Terms

Computer Science
Information Sciences

Keywords

Customer churn Telecommunication XG boost classifier Classification Churn Prediction

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