ML-Powered Career Guidance: A Web Application for Personalized Career Decision-Making

Abstract

Career decision-making is a crucial aspect of a student's academic journey, yet many struggle to identify the right career path due to a lack of personalized guidance. This research introduces a career guidance web application that leverages machine learning to provide tailored career recommendations. The platform gathers user data, including educational background, skills, and interests, to analyse and suggest suitable career options along with relevant skill recommendations. To enhance accuracy, the application employs two primary machine learning models: Random Forest, which predicts career paths with 87% accuracy, and TF- IDF, which identifies essential skills for professional growth. The frontend is developed using Next.js and Material UI to ensure a seamless user experience, while the backend, built with FastAPI and Python, handles data processing efficiently. Extensive testing confirms that the web application delivers reliable career recommendations and practical skill development insights, addressing the gap in traditional career counselling services. By integrating data-driven decision- making, this tool aims to empower students with personalized career guidance, enabling them to make informed academic and professional choices with confidence.