Machine Learning based Employee Layoff Prediction System

In the current fast-changing corporate environment, employee turnover and large-scale layoffs pose considerable challenges for Human Resource (HR) departments and organizational leaders. Employee turnover can result from personal dissatisfaction, low engagement, stagnation in career growth, or external job offers, while layoffs are frequently driven by financial limitations, automation, or strategic reorganization. Both situations affect company performance, employee morale, and operational stability. Conventional reactive strategies to manage attrition and layoffs often result in significant financial and productivity declines. To address this issue, a proactive, data-driven strategy is crucial. This project introduces a Machine Learning-based Employee Attrition Prediction and Layoff Prediction System, which is designed to analyze historical HR data and pinpoint employees who are at a heightened risk of voluntarily departing or being laid off in the future. The system utilizes employee records, encompassing demographic details, job performance, compensation, attendance, and satisfaction levels, to develop predictive models. Techniques such as Logistic Regression, Random Forest, Decision Tree, and Support Vector Machines are employed to categorize employees according to their attrition risk or layoff likelihood.