Abstract: Employee retention is a very important challenge to the organizations since it raises the cost of recruitment, affects domain knowledge retention, and impacts workforce stability. Presented here is a platform-based intelligent employee retention prediction system as a real-time HR decision support tool. As a part of the research, Feedforward Neural Network was initially trained and tested with structured employee data to confirm the relevance of the features and predictive viability, which had recorded an accuracy of 88.7%. This final implementation will integrate an AI-based Chat Widget with a modular pipeline system that will utilize an LLM for performing analytical reasoning on employee attributes and provide human-understandable explanations that will aid the HR decisions. The architecture separates the user interaction layer (Agent) from the prediction and reasoning logic (Pipeline), which makes the system scalable, interpretable, and easily integrable with the workflows of an organization. The proposed platform will show how validated predictive models and LLM-provided reasoning can be integrated in order to provide actionable and explainable employee retention insights.

Medha Wyawahare, Milind Rane, Ashish Rodi, Samarth Arole and Aryan Mundra. “Intelligent Platform for Employee Retention Prediction”. International Journal of Advanced Computer Science and Applications (IJACSA) 16.12 (2025). http://dx.doi.org/10.14569/IJACSA.2025.0161298

@article{Wyawahare2025,
title = {Intelligent Platform for Employee Retention Prediction},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.0161298},
url = {http://dx.doi.org/10.14569/IJACSA.2025.0161298},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {12},
author = {Medha Wyawahare and Milind Rane and Ashish Rodi and Samarth Arole and Aryan Mundra}
}