A Machine Learning Approach to Employee Performance Prediction within Administrative Information Systems

Aso Kurdo Ahmed · Saif Qudama Younus · Saadaldeen Rashid Ahmed · Sameer Saadoon Algburi · Mohammed Abdulraheem Fadhel

51 citations0/0 files storedInternational Symposium on Innovative Approaches in Smart Technologies (ISAS)

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AI and HR Technologies Online Learning and Analytics Data Mining and Machine Learning Applications

Abstract

This research project delves into the realm of employee performance prediction within organizational contexts, employing the k-Nearest Neighbors algorithm as the core predictive model. The central aim is to optimize organizational success by anticipating variations in individual performance metrics. Key features considered for prediction encompass the assessment of employee performance, average monthly work hours, and tenure with the company. Comparative analyses with alternative methodologies, such as k-Nearest Neighbors (KNN) algorithm, decision trees, and logistic regression, are conducted for a comprehensive evaluation. The dataset undergoes a meticulous 70-30 split for training and testing, resulting in a commendable accuracy of 97.32%. The implications of this research are significant for organizational management, providing a proactive tool to enhance and sustain employee performance, thereby fostering overall organizational success.

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References

2014Determinants of employee engagement and their impact on employee performance 2005The Effects of Flexibility in Employee Skills, Employee Behaviors, and Human Resource Practices on Firm Performance 2019Using machine learning to translate applicant work history into predictors of performance and turnover.2018Employee Turnover Prediction with Machine Learning: A Reliable Approach 2018Predicting Employee Attrition using Machine Learning 2021Machine learning and role of artificial intelligence in optimizing work performance and employee behavior 2023Analyzing The Employee Turnover by Using Decision Tree Algorithm 2019The role of internal environment factors in improving employee performance

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