Machine Learning-Based Employee Productivity Assessment Using Random Forest Classification

Employee productivity assessment is a critical challenge in organizational management, yet traditional methods often lack objectivity and scalability. We propose a data-driven approach that employs machine learning to classify employee productivity into High, Medium, or Low categories based on performance metrics such as task completion, working hours, attendance, and efficiency. The proposed system uses a Random Forest classifier, which aggregates predictions from multiple decision trees trained on randomized subsets of data and features, thereby improving robustness and accuracy. The methodology involves preprocessing employee data, splitting it into training and test sets, and training the model to predict productivity levels. Experimental results demonstrate that the system provides actionable insights for decision-making, enabling organizations to identify and address productivity bottlenecks effectively. Moreover, the Random Forest approach outperforms conventional methods by handling non-linear relationships and reducing overfitting. The significance of this work lies in its potential to transform subjective productivity evaluations into an automated, data-centric process, fostering fairer and more efficient workforce management. This study contributes to the growing body of research on machine learning applications in human resource analytics, offering a practical solution for modern enterprises.

Employee Productivity; Random Forest; Machine Learning

1. N Sharma & P Hosein (2020) A comparison of data-driven and traditional approaches to employee performance assessment. In 2020 International Conference On Big Data, Artificial Intelligence And Internet Of Things Engineering. [Google Scholar] [Crossref]

2. A Sharma & T Sharma (2017) HR analytics and performance appraisal system: A conceptual framework for employee performance improvement. Management Research Review. [Google Scholar] [Crossref]

3. MS Rehan, F Rustam, S Ullah, S Hussain, et al. (2022) Employees reviews classification and evaluation (ERCE) model using supervised machine learning approaches. Journal of Ambient Intelligence and Humanized Computing. [Google Scholar] [Crossref]

4. S Farhadpour, TA Warner & AE Maxwell (2024) Selecting and interpreting multiclass loss and accuracy assessment metrics for classifications with class imbalance: Guidance and best practices. Remote Sensing. [Google Scholar] [Crossref]

5. E Izquierdo-Verdiguier & R Zurita-Milla (2020) An evaluation of Guided Regularized Random Forest for classification and regression tasks in remote sensing. International Journal of Applied Earth Observation and Geoinformation. [Google Scholar] [Crossref]

6. S Dixit & K Sharma (2021) Performance management practices: A decisive approach to improve employee productivity. Turkish Journal of Computer and Mathematics. [Google Scholar] [Crossref]

7. B Bristol-Alagbariya, OL Ayanponle, et al. (2022) Developing and implementing advanced performance management systems for enhanced organizational productivity. researchgate.net. [Google Scholar] [Crossref]

8. I Ahmed, I Sultana, SK Paul & A Azeem (2013) Employee performance evaluation: A fuzzy approach. International Journal of Productivity and Performance Management. [Google Scholar] [Crossref]

9. S Pahmi, S Saepudin, N Maesarah, et al. (2018) Implementation of CART (classification and regression trees) algorithm for determining factors affecting employee performance. In International Conference on Computing, Communication, and Design. [Google Scholar] [Crossref]

10. EMTA Alsaadi, SF Khlebus & A Alabaichi (2022) Identification of human resource analytics using machine learning algorithms. Telkomnika. [Google Scholar] [Crossref]

11. H Jantan, NM Yusoff & MR Noh (2014) Towards applying support vector machine algorithm in employee achievement classification. Unable to determine the complete publication venue. [Google Scholar] [Crossref]

12. Z Nayem & MA Uddin (2024) Unbiased employee performance evaluation using machine learning. Journal of Open Innovation: Technology, Market, and Complexity. [Google Scholar] [Crossref]

13. A Agrawal, RA George, SS Ravi, et al. (2020) Leveraging multimodal behavioral analytics for automated job interview performance assessment and feedback. In Proceedings of the First Grand-Challenge and Workshop on Human Multimodal Language. [Google Scholar] [Crossref]

14. S Ishak & H Al-Deek (2002) Performance evaluation of short-term time-series traffic prediction model. Journal of Transportation Engineering. [Google Scholar] [Crossref]

15. L Raja & G Manoharan (2024) The Ethics of Machine Learning in Human Resources: Navigating Fairness and Efficiency. In 7th International Conference on Unspecified Topic. [Google Scholar] [Crossref]

16. SN Pletcher & L Boehme (2023) Practical and ethical perspectives on AI-based employee performance evaluation. OSF Preprints. [Google Scholar] [Crossref]

17. A Jääskeläinen (2010) Productivity measurement and management in large public service organizations. Tampere University of Technology. [Google Scholar] [Crossref]

18. M Balconi, S Brusoni & L Orsenigo (2010) In defence of the linear model: An essay. Research Policy. [Google Scholar] [Crossref]

19. RS Mor, A Bhardwaj, S Singh, et al. (2019) Productivity gains through standardization-of-work in a manufacturing company. Journal of Manufacturing Technology Management. [Google Scholar] [Crossref]

20. O Svenson (2011) Biased decisions concerning productivity increase options. Journal of Economic Psychology. [Google Scholar] [Crossref]

21. G Rusu, S Avasilcăi & CA Huţu (2016) Organizational context factors influencing employee performance appraisal: A research framework. Procedia - Social and Behavioral Sciences. [Google Scholar] [Crossref]

22. OZ Maimon & L Rokach (2014) Data mining with decision trees: theory and applications. books.google.com. [Google Scholar] [Crossref]

23. M Bramer (2020) Avoiding overfitting of decision trees. Principles of Data Mining. [Google Scholar] [Crossref]

24. AM Prasad, LR Iverson & A Liaw (2006) Newer classification and regression tree techniques: bagging and random forests for ecological prediction. Ecosystems. [Google Scholar] [Crossref]

25. HE Kiziloz (2021) Classifier ensemble methods in feature selection. Neurocomputing. [Google Scholar] [Crossref]

26. JG Gaudreault, P Branco & J Gama (2021) An analysis of performance metrics for imbalanced classification. In International Conference on Discovery Science. [Google Scholar] [Crossref]

27. CVG Zelaya (2019) Towards explaining the effects of data preprocessing on machine learning. In 2019 IEEE 35th International Conference On Data Engineering. [Google Scholar] [Crossref]

28. C Ruli & H Kristanto (2021) Implementation of balance scorecard and key performance indicator on customer service employee productivity. EUREKA: Social and Humanities, (4). [Google Scholar] [Crossref]

29. M Behery (2022) Single-rating, multi-rating 360 performance management and organizational outcomes: evidence from the UAE. International Journal of Organizational Analysis. [Google Scholar] [Crossref]

30. J Sadaiyandi, P Arumugam, AK Sangaiah & C Zhang (2023) Stratified sampling-based deep learning approach to increase prediction accuracy of unbalanced dataset. Electronics. [Google Scholar] [Crossref]

31. AA Nababan, M Zarlis, et al. (2024) Multiclass Logistic Regression Classification with PCA for Imbalanced Medical Datasets. Unable to determine the complete publication venue. [Google Scholar] [Crossref]

32. WY Loh (2011) Classification and regression trees. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery. [Google Scholar] [Crossref]

33. Y Tang, YQ Zhang, NV Chawla, et al. (2008) SVMs modeling for highly imbalanced classification. IEEE Transactions On Neural Networks. [Google Scholar] [Crossref]

34. G Rau & YS Shih (2021) Evaluation of Cohen’s kappa and other measures of inter-rater agreement for genre analysis and other nominal data. Journal of English for Academic Purposes. [Google Scholar] [Crossref]

35. SR Munoz & SI Bangdiwala (1997) Interpretation of Kappa and B statistics measures of agreement. Journal of Applied Statistics. [Google Scholar] [Crossref]

36. G Johns (2011) Attendance dynamics at work: the antecedents and correlates of presenteeism, absenteeism, and productivity loss. Journal of Occupational Health Psychology. [Google Scholar] [Crossref]

37. M Corbett (2015) From law to folklore: work stress and the Yerkes-Dodson Law. Journal of Managerial Psychology. [Google Scholar] [Crossref]

38. DA Kumari, IAK Shaikh, S Gangadharan, et al. (2024) Machine learning for diversity and inclusion: addressing biases in HR practices. Unable to determine the complete publication venue. [Google Scholar] [Crossref]

39. CH Yeh, RJ Willis, H Deng & H Pan (1999) Task oriented weighting in multi-criteria analysis. European Journal of Operational Research. [Google Scholar] [Crossref]

40. JL Pearce, WB Stevenson & JL Perry (1985) Managerial compensation based on organizational performance: A time series analysis of the effects of merit pay. Academy of Management Journal. [Google Scholar] [Crossref]

41. K Ball (2010) Workplace surveillance: An overview. Labor History. [Google Scholar] [Crossref]

42. T Sakhawat Hussain, T Rahanuma, et al. (2023) Privacy-Preserving Behavior Analytics for Workforce Retention Approach. Unable to determine the complete publication venue. [Google Scholar] [Crossref]

43. D Pessach & E Shmueli (2022) A review on fairness in machine learning. ACM Computing Surveys (CSUR). [Google Scholar] [Crossref]

44. J Huang (2025) Intelligent assessment of industrial green total factor productivity based on multimodal data fusion. In Proceedings of. [Google Scholar] [Crossref]

45. SV Pillai & J Prasad (2023) Investigating the key success metrics for WFH/remote work models. Industrial and Commercial Training. [Google Scholar] [Crossref]

46. AS Antonini, J Tanzola, L Asiain, GR Ferracutti, et al. (2024) Machine Learning model interpretability using SHAP values: Application to Igneous Rock Classification task. Applied Computing and Informatics. [Google Scholar] [Crossref]

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