Abstract: The transition after 12th standard is one of the most critical phases in a student’s academic journey, where choosing an appropriate career path often becomes challenging due to a lack of proper guidance and awareness. Traditional counseling methods are generally limited, generalized, and may be influenced by human bias. To overcome these limitations, this research proposes a Career Guidance System for Personalized Career Pathways that provides data-driven career recommendations based on student aptitude, academic performance, personal interests, and psychometric assessment results. The system utilizes Cosine Similarity and Decision Tree algorithms to analyze multiple parameters and generate personalized suggestions across domains such as Engineering, Medical, Commerce, Management, Arts, Design, and Government Services. Experimental evaluation on sample student profiles shows that the system delivers relevant and unbiased recommendations with improved decision accuracy compared to conventional counseling methods. The proposed system is scalable, cost effective, and user-friendly, making it a practical solution for students seeking informed career decisions.

Keywords: Career Guidance System, 12th Standard Students, Cosine Similarity, Decision Tree, Career Selection, Stream Selection (Science, Commerce, Arts), Personalized Career Recommendations, Data Analysis, Psychometric Assessment, Aptitude Analysis, Digital Platform, Accessibility, Career Counseling.

Downloads: | DOI: 10.17148/IJIREEICE.2026.14432