Future Navigator: Machine Learning Approaches to Career Planning

Abstract

This study presents a comprehensive approach to guiding individuals in selecting suitable career paths following higher secondary education. By leveraging a diverse dataset and employing a combination of handcrafted rules, clustering algorithms, and different modelling, we offer personalized career development recommendations. Additionally, we introduce a computerized career counselling system that utilizes objective assessments to predict optimal career paths based on factors such as skills assessment, interests, and academic performance. Our approach aims to minimize the likelihood of individuals choosing unsuitable career paths, thus enhancing the decision-making process for transitioning from education to the workforce. Through the integration of machine learning algorithms and objective assessments, this research contributes to advancing career guidance methodologies and empowering individuals in making informed career decisions..