Employee Turnover Analysis Using Machine Learning Algorithms

• URL: http://arxiv.org/abs/2402.03905v1
• Date: Tue, 6 Feb 2024 11:17:16 GMT
• Title: Employee Turnover Analysis Using Machine Learning Algorithms
• Authors: Mahyar Karimi, Kamyar Seyedkazem Viliyani
• Abstract summary: Machine learning techniques can be used to monitor and mitigate employee turnover risk. Three different supervised learning algorithms are used to benchmark employee attrition accuracy. Attained models can help out at establishing predictive analytics.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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