A Machine Learning-Based Career Prediction System for 12th Standard Students
Vaibhavi Vitthal Khamkar
Prof. Ramkrishna More Arts ,Commerce and Science College(Autonomous) Pradhikaran,
Pune -411044 India.
E-mail: vaibhavikhamkar@gmail.com
Ankush Dhamal Sir
Prof. Ramkrishna More Arts ,Commerce and Science College(Autonomous) Pradhikaran,
Pune -411044 India.
E-mail: ankushdhamal01@gmail.com
Abstract
Choosing an appropriate career path after completing the 12th standard is a critical decision for students, as it significantly influences their future academic and professional development. However, many students face confusion due to a lack of proper guidance, limited awareness of career options, and societal pressure. This research proposes a Machine Learning-Based Career Prediction System designed to assist 12th standard students in identifying suitable career paths based on their academic performance, interests, skills, and aptitude.
The proposed system utilizes machine learning algorithms to analyze student data collected through structured questionnaires and academic records. Features such as subject preferences, marks obtained in core subjects, personality traits, and career interests are used as input parameters. Multiple classification algorithms, including Decision Tree, Random Forest, and Support Vector Machine, are applied to predict the most appropriate career domain for each student. The system is trained and evaluated using a labeled dataset containing historical student profiles and their successful career outcomes.
The performance of the models is assessed using standard evaluation metrics such as accuracy, precision, recall, and F1-score. Experimental results demonstrate that the Random Forest model achieves higher prediction accuracy compared to other algorithms. The developed system provides personalized career recommendations through a user-friendly interface, helping students make informed decisions about higher education and career planning.
This study highlights the potential of machine learning in educational guidance systems and demonstrates how data-driven approaches can enhance career counseling for students at a crucial decision-making stage. The proposed system aims to reduce uncertainty in career selection and support students in aligning their strengths and interests with appropriate career opportunities.
Keywords: Machine Learning, Career Prediction, Student Guidance System, Classification Algorithms, Educational Data Mining.
