Improving Employee Retention Through Prediction and Risk Management Using Machine Learning
DOI:
https://doi.org/10.59890/ijarss.v2i6.1960Keywords:
Employee Retention, Employee turnover, HR Analytics, Random Forest, Machine LearningAbstract
This research investigates the effectiveness of two machine learning models (Logistic Regression and Random Forest) in predicting employee turnover. This research uses IBM HR Analytics employee attrition and performance dataset and performance dataset from Kaggle and implements nested ensemble models in Google Colab. After data pre-processing steps such as feature merging, generation, engineering, cleaning, coding, and normalisation, the data is divided into training and testing sets. The models were trained and evaluated based on their accuracy. The results of averaging the three departments showed that the Random Forest model achieved the highest accuracy (97.7%) compared to Logistic Regression (94.6%). Therefore, this study shows that Logistic Regression is the most suitable model to predict employee turnover in the given dataset.
References
Al-Darraji, S., Honi, D. G., Fallucchi, F., Abdulsada, A. I., Giuliano, R., & Abdulmalik, H. A. (2021). Employee attrition prediction using deep neural networks. Computers, 10(11), 1–11. https://doi.org/10.3390/computers10110141
Alshehhi, K., Zawbaa, S. Bin, Abonamah, A. A., & Tariq, M. U. (2021). Employee Retention Prediction in Corporate Organizations Using Machine Learning Methods. Academy of Entrepreneurship Journal, 27(SpecialIssue 2), 1–23.
Alshiddy, M. S., & Aljaber, B. N. (2023). Employee Attrition Prediction using Nested Ensemble Learning Techniques. International Journal of Advanced Computer Science and Applications, 14(7), 932–938. https://doi.org/10.14569/IJACSA.2023.01407101
Amin, V., Rathod, J. A., Kunder, M., & Patkar, P. (2021). A review on employee attrition using machine learning. International Research Journal of Modernization in Engineering Technology and Science, 03(05), 1237–1241. www.irjmets.com
Das, R. C., & Devi S.C, A. (2020). Conceptualizing the Importance of HR Analytics in Attrition Reduction. International Research Journal on Advanced Science Hub, 2(Special Issue ICAMET 10S), 40–48. https://doi.org/10.47392/irjash.2020.197
Khaliq, R., & Saritha, B. (2023). Examining the Influence of HR Analytics on the Performance of IT Companies. Journal of Economics, Management and Trade, 29(10), 215–223. https://doi.org/10.9734/jemt/2023/v29i101156
Letters, E. E., Singh, P., Shokeen, S., Raghava, V., Garg, S., Delhi, N., Delhi, N., Delhi, N., & Delhi, N. (2023). A Case Study on HR Analytics Employee Attrition Using Predictive Analytics. European Economic Letters, 13(5), 789–796. https://doi.org/10.52783/eel.v13i5.828
Raza, A., Munir, K., Almutairi, M., Younas, F., Muhammad, M., & Fareed, S. (2022). applied sciences Approaches. Applied Sciences.
Sanghavi, D., Parekh, J., Sompura, S., Kanani, P., & Professor, A. (2018). Data Visualization and Improving Accuracy of Attrition Using Stacked Classifier. International Journal of Engineering Development and Research, 6(4), 2321–9939. www.ijedr.org
Sari, S. F., & Lhaksmana, K. M. (2022). Employee Attrition Prediction Using Feature Selection with Information Gain and Random Forest Classification. Journal of Computer System and Informatics (JoSYC), 3(4), 410–419. https://doi.org/10.47065/josyc.v3i4.2099
Virani, Dr. F. (2023). Application of HR Analytics in Business. Met Management Review, 07(02), 05–19. https://doi.org/10.34047/mmr.2020.7201
Yahia, N. Ben, Hlel, J., & Colomo-Palacios, R. (2021). From Big Data to Deep Data to Support People Analytics for Employee Attrition Prediction. IEEE Access, 9, 60447–60458. https://doi.org/10.1109/ACCESS.2021.3074559
Yedida, R., Reddy, R., Vahi, R., Jana, R., GV, A., & Kulkarni, D. (2018). Employee Attrition Prediction. http://arxiv.org/abs/1806.10480
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Copyright (c) 2024 Galang Rintang Widya Pratama, Muhamad Fatchan, Wahyu Hadikristanto
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