Career Recommendation System with Skill Gap Analysis

Abstract

Selecting an appropriate career path has become increasingly challenging due to rapid changes in industries and the limited availability of personalised career guidance. Many existing career guidance tools rely on static questionnaires or traditional face-to-face counselling, which often fail to adapt to individual preferences and the dynamic requirements of the modern job market. This study proposes an intelligent career recommendation system that uses machine learning and natural language processing techniques to provide personalised career suggestions. The system analyses various user attributes, including skills, interests, educational background, and professional experience, to identify suitable career opportunities. By examining patterns within large datasets and comparing user profiles with job requirements, the model generates recommendations that reflect current labor market trends. In addition to recommending relevant career paths, the system also identifies skill gaps and highlights areas where users can improve their competencies to achieve their career goals. The proposed approach aims to assist students and job seekers in making informed career decisions based on data-driven insights rather than assumptions. Experimental evaluation demonstrates that the system improves the relevance and accuracy of career recommendations while simplifying the decision-making process. Overall, the model provides a flexible and effective solution for modern career guidance in a rapidly evolving employment and scape.