Comprehensive Study on Employee Promotion Using Classification Techniques

Abstract: Promoting employees is an essential procedure in organizational frameworks that directly affects motivation, productivity, and retention of the workforce. This research investigates data-centric approaches for forecasting employee advancements through classification methods. A collection of 1,000 employee records was examined, featuring variables with 12 like education, age, training scores, last year’s rating, and department. Following preprocessing to manage absent values and encode categorical variables, models such as Logistic Regression, Decision Tree, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Gaussian Naive Bayes (GNB) were created and assessed. Performance metrics including accuracy, precision, recall, specificity, and F1 score were utilized to evaluate model results. GNB proved to be the best model, achieving an accuracy of 83% on the test data, demonstrating resilience despite class imbalance. The study finds that statistical learning methods can greatly assist human resource departments in making informed, fair, and efficient decisions regarding promotions.

Employee Promotion, Classification, Gaussian Naive Bayes, Human Resource Analytics, Data Mining, KNN, Logistic regression, Graphical visualization

1. Ansari, N., &Vora, N. (2024). “Employee Promotion Evaluation and Prediction Using Machine Learning”. Journal of Information Technology and Digital World, 6(4), 317–332. [Google Scholar] [Crossref]

2. Asim, Y., Raza, B., Malik, A. K., Rathore, S., & Bilal, A. (2018). “Improving the Performance of Professional Blogger's Classification”. International Conference on Engineering Technologies, Mathematics, and Computing (iCOMET), Sukkur. [Google Scholar] [Crossref]

3. Huang, Y., Shum, M., Wu, X., & Xiao, J. Z. (2019), Discovery of Bias and Strategic Behavior in Crowdsourced Performance Assessment. arXiv. [Google Scholar] [Crossref]

4. Han, J., Kamber, M., & Pei, J. (2011),"Data Mining: Concepts and Techniques." [Google Scholar] [Crossref]

5. Ilwani, M., Nassreddine, G., & Younis, J. (2023). “Machine Learning Application on Employee Promotion”. Mesopotamian Journal of Computer Science. [Google Scholar] [Crossref]

6. Kelleher, J. D., Mac Carthy, M., & Tierney, B. (2015)."Fundamentals of Machine Learning for Predictive Data Analytics." [Google Scholar] [Crossref]

7. Nosratabadi, S., Zahed, R. K., Ponkratov, V. V., & Kostyrin, E. V. (2022). “Artificial Intelligence Models and Employee Lifecycle Management: A Systematic Literature Review”. arXiv. [Google Scholar] [Crossref]

8. Purwandari, B., Ruldeviyani, Y., &Ramdhani, T. W. (2016). “The Use of Data Mining Classification Technique to Fill in Structural Positions in Bogor Local Government”. International Conference on Advanced Computer Science and Information Systems (ICACSIS), Malang. [Google Scholar] [Crossref]

9. Shafie, S. S., Ooi, S. P., & Khaw, K. W. (2023). “Prediction of Employee Promotion Using Hybrid Sampling Method with Machine Learning Architecture”. Malaysian Journal of Computing. [Google Scholar] [Crossref]

10. Subramanian, P., & Suresh, R. (2018)."Employee promotion prediction using machine learning techniques. “Proceedings of the International Conference on Computer Communication and Informatics (ICCCI). [Google Scholar] [Crossref]

11. Parmar, D., et al. (2020)."Predictive Analytics in Human Resource Management using Machine [Google Scholar] [Crossref]

12. Learning Techniques."International Journal of Computer Applications. [Google Scholar] [Crossref]

13. Link: https://drive.google.com/file/d/1DjsdcaptBZ52BXM_NoxuHA2ciMGrgmVe/view?usp=drive_link For secondary data. [Google Scholar] [Crossref]

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