Employee Turnover Analysis Using Machine Learning Algorithms
This addresses employee turnover risk for organizations, but it is incremental as it applies existing methods to a common domain-specific problem.
The paper tackled employee turnover prediction by benchmarking three supervised learning algorithms (AdaBoost, SVM, RandomForest) to analyze attrition rates, achieving models for predictive analytics.
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.