PERSONALIZED CAREER RECOMMENDATION SYSTEM USING MACHINE LEARNING

Choosing the right career is a significant decision, especially for students and young professionals who face increasing uncertainty due to the wide range of career options and the constantly changing demands of the job market.Traditional career counseling methods often lack personalization and fail to consider an individual's unique attributes.This study presents a personalized career recommendation system using machine learning to offer tailored career suggestions based on academic performance, skills, interests, personality traits, and extracurricular activities.A comprehensive dataset was compiled from academic records, psychometric assessments, and user-submitted profiles.Several supervised machine learning algorithms including Decision Trees, Random Forest, Support Vector Machines (SVM), K-Nearest Neighbors (KNN), and Neural Networks were implemented and compared to determine the most effective model.Feature preprocessing techniques such as normalization, one-hot encoding, and Principal Component Analysis (PCA) were applied to improve model accuracy and performance.The system provides users with a ranked list of suitable career paths along with interpretability features that explain the rationale behind each recommendation.Experimental results demonstrate that machine learning techniques significantly improve the accuracy and relevance of career guidance.The proposed system offers a scalable, data-driven solution and has potential for integration into educational platforms to assist users in making informed career decisions