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: firstname.lastname@example.org, Asmam3008@gmail.com, Munirah.email@example.com, Atheer.firstname.lastname@example.org, Ruyan.email@example.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: firstname.lastname@example.org).
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).