Abstract
<jats:p>This study presents a novel machine learning approach aimed at identifying features critical for determining the 'Cost to the Company' (CTC) of management graduates. With the increasing complexity of job markets and the diversity of roles undertaken by management professionals, accurately assessing the CTC becomes paramount for both employers and employees. Traditional methods often rely on simplistic metrics or subjective assessments, leading to potential inaccuracies and biases. In contrast, this approach leverages advanced machine learning techniques to analyze a comprehensive dataset encompassing various factors such as academic background, skills, internships, industry exposure, and extracurricular activities. Through feature selection algorithms and predictive modeling, the authors aim to elucidate the most influential factors contributing to CTC determination. By identifying these critical features, the methodology not only enhances the precision of CTC estimation but also provides valuable insights for career planning and talent management strategies within the management domain.</jats:p>