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Predictive Framework for Advanced Customer Churn Prediction using Machine Learning

by Dinesh Kumar Jena, Abhyarthana Bisoyi, Aruna Tripathy
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 185 - Number 13
Year of Publication: 2023
Authors: Dinesh Kumar Jena, Abhyarthana Bisoyi, Aruna Tripathy
10.5120/ijca2023922818

Dinesh Kumar Jena, Abhyarthana Bisoyi, Aruna Tripathy . Predictive Framework for Advanced Customer Churn Prediction using Machine Learning. International Journal of Computer Applications. 185, 13 ( Jun 2023), 43-51. DOI=10.5120/ijca2023922818

@article{ 10.5120/ijca2023922818,
author = { Dinesh Kumar Jena, Abhyarthana Bisoyi, Aruna Tripathy },
title = { Predictive Framework for Advanced Customer Churn Prediction using Machine Learning },
journal = { International Journal of Computer Applications },
issue_date = { Jun 2023 },
volume = { 185 },
number = { 13 },
month = { Jun },
year = { 2023 },
issn = { 0975-8887 },
pages = { 43-51 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume185/number13/32760-2023922818/ },
doi = { 10.5120/ijca2023922818 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:26:00.893347+05:30
%A Dinesh Kumar Jena
%A Abhyarthana Bisoyi
%A Aruna Tripathy
%T Predictive Framework for Advanced Customer Churn Prediction using Machine Learning
%J International Journal of Computer Applications
%@ 0975-8887
%V 185
%N 13
%P 43-51
%D 2023
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In recent years, the telecom sector has been burgeoning to satisfy the demand of mobile subscribers and telecom service providers. The increase in number of mobile subscribers and competition among providers, results in the creation of “churners”. These are the subscribers who tend to switch from the current telecom service to another. The detection of these churners is called “churn prediction”. This prediction has become a major challenge for telecom companies. The main purpose of customer churn prediction is to estimate the number of subscribers those who want to quit the current service provider by providing specific solutions to retain them. This paper proposes methods for the estimation of churners by applying different classification techniques and estimates the differences between them. The performance is measured by taking different parameters like accuracy, precision, recall, etc. In this paper, the various performance measurement and comparison are done by using the dataset collected from American Telecommunication Company. All the proposed work is based on Machine Learning, inculcating the supervised learning. In addition to all, a single test-bed is designed as a user interface to predict the individual customer according to different attributes.

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Index Terms

Computer Science
Information Sciences

Keywords

BigML Churning Customer churn prediction Decision tree Machine Learning Random Forest Supervised learning Telecom User Interface

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