Open Access
Analyzing employee attrition using decision tree algorithms
D Alao,A. B. Adeyemo +1 more
- Vol. 4, Iss: 1
TLDR
In this paper, a study identifies employee related attributes that contribute to the prediction of employees' attrition in organizations, and a framework for a software tool that can implement therules generated in this study was also proposed.Abstract:
Employee turnover is a serious concern in knowledge based organizations. When employees leave an organization, theycarry with them invaluable tacit knowledge which is often the source of competitive advantage for the business. In order foran organization to continually have a higher competitive advantage over its competition, it should make it a duty to minimizeemployee attrition. This study identifies employee related attributes that contribute to the prediction of employees’ attritionin organizations. Three hundred and nine (309) complete records of employees of one of the Higher Institutions in Nigeriawho worked in and left the institution between 1978 and 2006 were used for the study. The demographic and job relatedrecords of the employee were the main data which were used to classify the employee into some predefined attrition classes.Waikato Environment for Knowledge Analysis (WEKA) and See5 for Windows were used to generate decision tree modelsand rule-sets. The results of the decision tree models and rule-sets generated were then used for developing a a predictivemodel that was used to predict new cases of employee attrition. A framework for a software tool that can implement therules generated in this study was also proposed.Keywords: Employee Attrition, Decision Tree Analysis, Data Miningread more
Citations
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Journal ArticleDOI
Prediction of Employee Turnover in Organizations using Machine Learning Algorithms
Rohit Punnoose,Pankaj Ajit +1 more
TL;DR: The novel contribution of this paper is to explore the application of extreme gradient boosting (XGBoost) as an improvement on these traditional algorithms, specifically in its ability to generalize on noise-ridden data which is prevalent in this domain.
Book ChapterDOI
Employee Turnover Prediction with Machine Learning: A Reliable Approach.
TL;DR: Through a robust and comprehensive evaluation process, the performance of each of these supervised machine learning methods for predicting employee turnover is analyzed and established using statistical methods.
Journal ArticleDOI
Predicting Employee Attrition Using Machine Learning Techniques
TL;DR: The goal of this work is to analyse how objective factors influence employee attrition, in order to identify the main causes that contribute to a worker’s decision to leave a company, and to be able to predict whether a particular employee will leave the company.
Proceedings ArticleDOI
Predicting Employee Attrition using Machine Learning
Sarah S. Alduayj,Kashif Rajpoot +1 more
TL;DR: This research studies employee attrition using machine learning models using a synthetic data created by IBM Watson to predict employee attrition.
Proceedings ArticleDOI
Evaluation of machine learning models for employee churn prediction
TL;DR: This paper tries to build a model which will predict employee churn rate based on HR analytics dataset obtained from Kaggle website, and proposes the reasons which optimize the employee attrition in any organization.
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