AI-Powered Career Recommendation System

Abstract- In today’s competitive and rapidly evolving job market, selecting an appropriate career path is increasingly challenging for students and professionals. Traditional career guidance methods, often based on static aptitude tests, human counsellors, or outdated datasets, fail to account for dynamic industry trends, emerging job roles, and skill demands. This research presents an AI-powered Career Recommendation System that leverages machine learning (ML), natural language processing (NLP), and data analytics to provide personalized and adaptive career suggestions. The system collects user information, including academic performance, technical and soft skills, interests, and work experience, and predicts suitable career domains using AI-driven classification and recommendation models. A dataset comprising thousands of job descriptions, skill mappings, and industry requirements was compiled to train supervised ML models, including Random Forest, Naive Bayes, and neural networks. NLP techniques, such as TF-IDF vectorization and word embeddings, convert unstructured skill and interest inputs into numeric feature vectors for accurate prediction. Built on a MERN (MongoDB, Express.js, React.js, Node.js) stack, the platform enables real-time interaction, personalized dashboards, and AI-assisted career counselling. Pilot experiments demonstrate that hybrid recommendation models combined with content-based filtering and ML classification improve prediction accuracy, user engagement, and satisfaction compared to conventional career guidance approaches. This research highlights the potential of AI in bridging the gap between learning and employability, providing users with data-driven insights to make informed career decisions, while also offering a scalable and secure solution for institutions and career guidance platforms. Keyword- Artificial Intelligence, Career Guidance, Machine Learning, Recommendation System, Natural Language Processing, Career Prediction, MERN Stack, Personalization