Employee Attrition Prediction using Ensemble Methods

Employee attrition, or employees quitting the firm willingly, is a persistent problem for contemporary businesses. This is a serious issue for businesses, especially when critical personnel like qualified technicians depart for more advantageous positions. Financial losses are incurred to replace the skilled workforce as a result. When employees quit their jobs, they typically carry valuable, unspoken knowledge with them that offers the organization a competitive advantage. A corporation should prioritize reducing personnel attrition with the goal to maintain a persistent competitive advantage over its competitors. This study focuses on this, which will help businesses estimate staff turnover and promote economic growth. This study investigates employee attrition prediction analysis to address the pressing issue of voluntary turnover in contemporary businesses. In this work, four ensemble techniques were used, mainly achieving an accuracy of 88.73% for the MLP, Random Forest, and KNN ensemble. This study emphasizes the value of proactive retention methods for fostering a healthy workplace environment and guaranteeing organizational stability.