Career Prediction Website Using Machine Learning

Abstract

In today’s rapidly evolving educational landscape, students often face significant uncertainty when choosing suitable career paths after completing their higher secondary education. Traditional methods of career counseling rely heavily on subjective judgment, one-time assessments, or generic suggestions that fail to consider the unique capabilities, interests, and learning trajectories of each student. This project proposes the development of an intelligent, machine learning-based Career Guidance System designed to offer personalized and data-driven career recommendations. By leveraging supervised and unsupervised learning algorithms—including Decision Trees, Support Vector Machines, and XG Boost—the system evaluates academic records, skill assessments, and psychometric inputs to predict the most suitable academic streams and career domains. The system also incorporates a conversational chatbot to improve user interaction and adaptability, ensuring continuous learning and feedback. The proposed model aims to bridge the gap between student potential and career decision-making, offering scalable, real-time, and insightful guidance to empower students in making informed academic and professional choices.