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IJMLC 2021 Vol.11(2): 110-114 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2021.11.2.1022

Prediction of Employee Attrition Using Machine Learning and Ensemble Methods

Aseel Qutub, Asmaa Al-Mehmadi, Munirah Al-Hssan, Ruyan Aljohani, and Hanan S. Alghamdi

Abstract—Employees are the most valuable resources for any organization. The cost associated with professional training, the developed loyalty over the years and the sensitivity of some organizational positions, all make it very essential to identify who might leave the organization. Many reasons can lead to employee attrition. In this paper, several machine learning models are developed to automatically and accurately predict employee attrition. IBM attrition dataset is used in this work to train and evaluate machine learning models; namely Decision Tree, Random Forest Regressor, Logistic Regressor, Adaboost Model, and Gradient Boosting Classifier models. The ultimate goal is to accurately detect attrition to help any company to improve different retention strategies on crucial employees and boost those employee satisfactions.

Index Terms—Employee attrition, ensemble learning, gradient boosting classifier, machine learning, random forest regressor, stochastic gradient decent.

Aseel Qutub, Asmaa Al-Mehmadi, Munirah Al-Hssan, Ruyan Aljohani were with the King Abdulaziz University, Information Systems Department, Faculty of Computing and Information Technology Jeddah, Saudi Arabia (e-mail: aseel.qutub@gmail.com, Asmam3008@gmail.com, Munirah.h.hassan@gmail.com, Atheer.kh10@gmail.com, Ruyan.n191@gmail.com).
Hanan. S. Alghamdi Author is with the King Abdulaziz University, Information Systems Department, Faculty of Computing and Information Technology Jeddah, Saudi Arabia (e-mail: hsaalghamdi@kau.edu.sa).

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Cite: Aseel Qutub, Asmaa Al-Mehmadi, Munirah Al-Hssan, Ruyan Aljohani, and Hanan S. Alghamdi, "Prediction of Employee Attrition Using Machine Learning and Ensemble Methods," International Journal of Machine Learning and Computing vol. 11, no. 1, pp. 110-114, 2021.

Copyright © 2021 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

General Information

  • ISSN: 2010-3700 (Online)
  • Abbreviated Title: Int. J. Mach. Learn. Comput.
  • Frequency: Bimonthly
  • DOI: 10.18178/IJMLC
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals Library.
  • E-mail: ijmlc@ejournal.net


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