Employee Turnover Analysis Using Machine Learning Algorithms

Employee's knowledge is an organization asset. Turnover may impose apparent and hidden costs and irreparable damages. To overcome and mitigate this risk, employee's condition should be monitored. Due to high complexity of analyzing well-being features, employee's turnover predicting c...

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Bibliographic Details
Published in:arXiv.org 2024-02
Main Authors: Karimi, Mahyar, Viliyani, Kamyar Seyedkazem
Format: Article
Language:English
Subjects:
Algorithms
Machine learning
Machinery condition monitoring
Supervised learning
Online Access:Get full text
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Summary:Employee's knowledge is an organization asset. Turnover may impose apparent and hidden costs and irreparable damages. To overcome and mitigate this risk, employee's condition should be monitored. Due to high complexity of analyzing well-being features, employee's turnover predicting can be delegated to machine learning techniques. In this paper, we discuss employee's attrition rate. Three different supervised learning algorithms comprising AdaBoost, SVM and RandomForest are used to benchmark employee attrition accuracy. Attained models can help out at establishing predictive analytics.
ISSN:2331-8422