Authors

Mohan Reddy Sareddy

Orasys LLC,Texas, USA

R.Pushpakumar

Department of Information Technology, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Tamil Nadu, Chennai, India

Abstract

HRM is traveling on the paths of digitalization from the past tradition-based intuition-dominated processes towards the data-driven frameworks for decision making. The present study investigates the application of predictive analytics and in particular, the Random Forest algorithm in correspondence to exploratory data analysis to strengthen the decision making in HR. It has proposed an organized pipeline of data collection and preprocessing followed by descriptive statistics, visualization, and machine learning for forecasting two primary HR outcomes: employee retention and performance. The results of the analysis brought forth specifically the finding of bimodal distributions in scores of performance and absence of direct correlation of the dimensions under consideration, namely salary and performance, highlighting the role of non-monetary motivators. The Random Forest Model had an excellent classification accuracy of an AUC of .99 to output engagement score, promotion history, and age, as major predictors in regard to retention. Immense promise lies in predictive analytics, but barriers of data inconsistency, deficiency in analytics literacy, and system integration issues remain. Hence, this research proposes a paradigm shift to data governance with a focus on analytics education and visualization-based decision support to maximize predictive HR analytics for organizational performance and workforce optimization.

Keywords

HRM Analytics Employee Retention Predictive Analytics Predictive Modeling Random Forest

Citation of this Article

Mohan Reddy Sareddy, & R.Pushpakumar. (2025). Enhancing HR Decision-Making Using AI, Random Forests, and Exploratory Data Analysis. Current Journal of Engineering and Science Research. 2(2), 26-34. Article DOI: https://doi.org/10.47001/CJESR/2025.202006  

Licence Copyright (c) 2026 Current Journal of Engineering and Science Research. This work is licensed under a Creative Commons Attribution Non Commercial 4.0 International Licence.

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