Abstract: Career decision-making is a complex process that significantly influences an individual’s academic progression and long-term professional success, yet traditional career guidance approaches rely heavily on manual counseling, static assessments, and generalized recommendations that often fail to account for the diverse abilities, interests, and evolving aspirations of students. To overcome these limitations, this project proposes an AI-Powered Career Guidance and Recommendation System that leverages machine learning techniques to deliver personalized and data-driven career recommendations. The system collects structured student information, including academic performance, technical and soft skills, areas of interest, and personality-related attributes, which is then subjected to comprehensive preprocessing steps such as data cleaning, categorical encoding, numerical normalization, and feature selection to ensure compatibility with machine learning models. Multiple supervised learning algorithms are trained and evaluated using performance metrics including accuracy, precision, recall, and F1-score, with the most effective model selected for deployment. The trained model predicts suitable career domains and generates ranked career recommendations tailored to individual profiles, while model persistence and a modular system architecture support scalability, consistency, and future retraining. Experimental results demonstrate that the proposed system provides accurate and reliable career recommendations, highlighting the effectiveness of machine learning in career guidance applications, reducing dependence on manual counseling, and improving accessibility to consistent, objective, and intelligent career decision-support services.

Keywords: AI-Powered Career Guidance, Career Recommendation System, Machine Learning, Student Profiling, Supervised Learning, Random Forest, Feature Engineering, Clustering Techniques, Data Preprocessing, Model Persistence, Joblib, Streamlit, Decision Support System, Educational Data Mining, Personalized Career Prediction.

Downloads: | DOI: 10.17148/IARJSET.2026.13153