The Science and Information (SAI) Organization
  • Home
  • About Us
  • Journals
  • Conferences
  • Contact Us

Publication Links

  • IJACSA
  • Author Guidelines
  • Publication Policies
  • Digital Archiving Policy
  • Promote your Publication
  • Metadata Harvesting (OAI2)

IJACSA

  • About the Journal
  • Call for Papers
  • Editorial Board
  • Author Guidelines
  • Submit your Paper
  • Current Issue
  • Archives
  • Indexing
  • Fees/ APC
  • Reviewers
  • Apply as a Reviewer

IJARAI

  • About the Journal
  • Archives
  • Indexing & Archiving

Special Issues

  • Home
  • Archives
  • Proposals
  • Guest Editors
  • SUSAI-EE 2025
  • ICONS-BA 2025
  • IoT-BLOCK 2025

Future of Information and Communication Conference (FICC)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Computing Conference

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Intelligent Systems Conference (IntelliSys)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Future Technologies Conference (FTC)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact
  • Home
  • Call for Papers
  • Editorial Board
  • Guidelines
  • Submit
  • Current Issue
  • Archives
  • Indexing
  • Fees
  • Reviewers
  • Subscribe

DOI: 10.14569/IJACSA.2021.0120320
PDF

Model for Predicting Customer Desertion of Telephony Service using Machine Learning

Author 1: Carlos Acero-Charaña
Author 2: Erbert Osco-Mamani
Author 3: Tito Ale-Nieto

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 12 Issue 3, 2021.

  • Abstract and Keywords
  • How to Cite this Article
  • {} BibTeX Source

Abstract: In the present study, it is observed that many people are affected by the services provided by telephony, who leave the service for different reasons, for which the use of a model based on decision trees is proposed, which allows predicting potential dropouts from Customers of a telecommunications company for telephone service. To verify the results, several algorithms were used such as neural networks, support vector machine and decision trees, for the design of the predictive models the KNIME software was used, and the quality was evaluated as the percentage of correct answers in the predicted variable. The results of the model will allow acting proactively in the retention of clients and improves the services provided. A data set with 21 predictor variables that influence customer churn was used. A dependent variable (churn) was used, which is an identifier that determines if the customer left = 1, did not leave = 0 the company's service. The results with a test data set reach a precision of 91.7%, which indicates that decision trees turn out to be an attractive alternative to develop prediction models of customer attrition in this type of data, due to the simplicity of interpretation of the results.

Keywords: Software KNIME; Support Vector Machine (SVM); neural networks; decision trees

Carlos Acero-Charaña, Erbert Osco-Mamani and Tito Ale-Nieto, “Model for Predicting Customer Desertion of Telephony Service using Machine Learning” International Journal of Advanced Computer Science and Applications(IJACSA), 12(3), 2021. http://dx.doi.org/10.14569/IJACSA.2021.0120320

@article{Acero-Charaña2021,
title = {Model for Predicting Customer Desertion of Telephony Service using Machine Learning},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2021.0120320},
url = {http://dx.doi.org/10.14569/IJACSA.2021.0120320},
year = {2021},
publisher = {The Science and Information Organization},
volume = {12},
number = {3},
author = {Carlos Acero-Charaña and Erbert Osco-Mamani and Tito Ale-Nieto}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

IJACSA

Upcoming Conferences

IntelliSys 2025

28-29 August 2025

  • Amsterdam, The Netherlands

Future Technologies Conference 2025

6-7 November 2025

  • Munich, Germany

Healthcare Conference 2026

21-22 May 2026

  • Amsterdam, The Netherlands

Computing Conference 2026

9-10 July 2026

  • London, United Kingdom

IntelliSys 2026

3-4 September 2026

  • Amsterdam, The Netherlands

Computer Vision Conference 2026

15-16 October 2026

  • Berlin, Germany
The Science and Information (SAI) Organization
BACK TO TOP

Computer Science Journal

  • About the Journal
  • Call for Papers
  • Submit Paper
  • Indexing

Our Conferences

  • Computing Conference
  • Intelligent Systems Conference
  • Future Technologies Conference
  • Communication Conference

Help & Support

  • Contact Us
  • About Us
  • Terms and Conditions
  • Privacy Policy

© The Science and Information (SAI) Organization Limited. All rights reserved. Registered in England and Wales. Company Number 8933205. thesai.org