Automated Prediction of Employee Attrition Using Ensemble Model Based on Machine Learning Algorithms

Fahad Kamal Alsheref · Ibrahim Eldesouky Fattoh · Waleed Mahmoud Ead

59 citations0/0 files storedComputational Intelligence and Neuroscience

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AI and HR Technologies Online Learning and Analytics Customer churn and segmentation

Abstract

Competent employees are a rare commodity for great companies. The problem of maintaining good employees with experience threatens the owners of companies. The issue of employee attrition can cost employers a lot as it takes a lot to compensate for their expertise and efficiency. For this reason, in this research, we present an automated model that can predict employee attrition based on different predictive analytical techniques. These techniques have been applied with different pipeline architectures to select the best champion model. Also, an autotuning approach has been implemented to calculate the best combination of hyper parameters to build the champion model. Finally, we propose an ensemble model for selecting the most efficient model subject to different assessments measures. The results of the proposed model show that no model up until now could be considered ideal and perfect for each case of business context. Yet, our chosen model was pretty much optimal as per our requirements and adequately satisfied the intended goal.

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References

1979A Formula for the Gini Coefficient 2020Predicting Employee Attrition Using Machine Learning Techniques 2016Prediction of Employee Turnover in Organizations using Machine Learning Algorithms 1979Health and Labor Market Success: The Role of Various Diseases 2018Employee Turnover Prediction with Machine Learning: A Reliable Approach 2018Predicting Employee Attrition using Machine Learning 2017Evaluation of machine learning models for employee churn prediction 2020Explaining and predicting employees’ attrition: a machine learning approach

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2024Enhancing the Prediction of Employee Turnover With Knowledge Graphs and Explainable AI 2023Convolutional Neural Network and Capsule Network Fusion for Effective Attrition Classification 2025Employee Retention Analytics: Predictive Modeling for Workforce Stability and Talent Management 2025A Deep Learning Model Based on Bidirectional Temporal Convolutional Network (Bi-TCN) for Predicting Employee Attrition 2023AI in HRM: case study analysis. Preliminary research

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