SmartHR: AI-Based Employee Retention and Exit Prediction System Using Machine Learning

Abstract

Employee turnover is a significant problem for contemporary businesses, because it raises operating expenses and reduces organizational productivity. Losing seasoned workers results in higher costs for hiring, onboarding, and training new personnel. Organizations need intelligent technologies that can identify workers who might quit the company soon in order to lower these risks. This study presents Smart HR, an artificial intelligence-based solution for employee retention and exit prediction that uses machine learning approaches to assess attrition probability and evaluate employee-related data. The system analyzes a dataset that includes characteristics like age, department, performance rating, work happiness, wage level, and number of projects issued. Prior to training the predictive models, data preprocessing methods such as cleaning, normalization, and feature selection are used. Employees are then categorized according to the probability of retention or resignation using machine learning algorithms. The created model is incorporated into an interactive application that enables HR managers to input personnel information and obtain real-time forecasts and suggestions for enhancing retention. Predictive analytics can successfully identify trends linked to employee behavior and help businesses make well-informed human resource decisions, according to experimental investigation. By assisting companies in identifying attrition concerns early on and putting appropriate retention tactics into place, the suggested method promotes proactive workforce management.