Earticle

Employee placement is one of the vital functions of HR, which aligns the employee's skill with the organization's requirements. Traditional methods of placement have many shortcomings: skill mismatching, bias, and underutilization or misutilization of resources. The study uses machine learning (ML) to these problems by evaluating three algorithms-AdaBoost, support vector machine (SVM), and CatBoost-with demographic and job-related data from Kaggle. Results indicate that the maximum accuracy AdaBoost reached was 86%, then SVM with 81.4%, followed by CatBoost at 79%. These findings point to the reliability of AdaBoost in structured data and emphasize the potential that ML has for improving HR efficiency, employee satisfaction, and retention.