Machine Learning-Based Career Recommendation System Using User Interests and Behavioural Analysis

Abstract

Career choice is an important but often confusing decision for students and people who are laying the path for their future in today’s fast-paced and competitive world. There are so many professional options out there that many find it difficult to identify a career that aligns with their true interests, skills, and long-term goals. The project aims to provide a smart, data-driven solution to make this important decision much easier through a machine learning-based career recommendation system. The system works by analyzing users' hobbies and interests, as well as their responses to a well-designed set of binary (yes/no) questions. Then, the answers are processed by a tree-based classification model to determine the suitable career fields for each person. It provides customized career suggestions in a user-friendly manner, considering each user's preferences and behavioral patterns. This approach demonstrates the increasing potential of artificial intelligence in educational guidance and counseling. This system provides a faster, more flexible, and scalable path to career exploration than traditional aptitude tests or manual evaluations. The results of this project demonstrate how interests and passions, combined with smart algorithms, can provide useful insights into possible future professional opportunities. In the end, this tool helps people make more informed, confident, and meaningful career decisions.