Predicting Employee Attrition using Machine Learning

Sarah S. Alduayj, Kashif Rajpoot

Research output: Chapter in Book/Report/Conference proceedingConference contribution

21 Citations (Scopus)

Abstract

The growing interest in machine learning among business leaders and decision makers demands that researchers explore its use within business organisations. One of the major issues facing business leaders within companies is the loss of talented employees. This research studies employee attrition using machine learning models. Using a synthetic data created by IBM Watson, three main experiments were conducted to predict employee attrition. The first experiment involved training the original class-imbalanced dataset with the following machine learning models: support victor machine (SVM) with several kernel functions, random forest and K-nearest neighbour (KNN). The second experiment focused on using adaptive synthetic (ADASYN) approach to overcome class imbalance, then retraining on the new dataset using the abovementioned machine learning models. The third experiment involved using manual undersampling of the data to balance between classes. As a result, training an ADASYN-balanced dataset with KNN (K = 3) achieved the highest performance, with 0.93 F1-score. Finally, by using feature selection and random forest, F1-score of 0.909 was achieved using 12 features out of a total of 29 features.

Original languageEnglish
Title of host publicationProceedings of the 2018 13th International Conference on Innovations in Information Technology, IIT 2018
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages93-98
Number of pages6
ISBN (Electronic)9781538666739
DOIs
Publication statusPublished - 8 Jan 2019
Event13th International Conference on Innovations in Information Technology, IIT 2018 - Al Ain, United Arab Emirates
Duration: 18 Nov 201819 Nov 2018

Publication series

NameProceedings of the 2018 13th International Conference on Innovations in Information Technology, IIT 2018

Conference

Conference13th International Conference on Innovations in Information Technology, IIT 2018
Country/TerritoryUnited Arab Emirates
CityAl Ain
Period18/11/1819/11/18

Bibliographical note

Publisher Copyright:
© 2018 IEEE.

Keywords

  • Employee attrition
  • Feature ranking
  • Feature selection
  • K nearest neighbours
  • Machine learning
  • random forest
  • Support vector machine

ASJC Scopus subject areas

  • Hardware and Architecture
  • Information Systems and Management
  • Information Systems

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