Personalized Career Planning using an Intelligent Recommendation with Dynamic Roadmaps
DOI:
https://doi.org/10.15662/IJEETR.2026.0802239Keywords:
Career Recommendation, Random Forest, Progressive Learning, Gamification, Personalized Learning PathsAbstract
Career guidance for students remains challenging due to lack of personalization and scalability in traditional counseling methods. This paper presents an intelligent career recommendation system using Random Forest machine learning algorithm enhanced with comprehensive 25-question assessment framework. The system generates personalized top-3 career recommendations with confidence scores and dynamic learning roadmaps featuring progressive modules (Foundation>Intermediate>Advanced>Professional) integrated with gamification elements. Implemented on React-Flask-MySQL architecture, our system achieves 88% classification accuracy with sub-500ms response latency, enabling real-time career guidance at scale. Feature importance analysis provides transparency in recommendations while progressive module unlocking ensures structured skill development. Experimental evaluation demonstrates superior performance compared to traditional weight-based systems. The system demonstrates practical deployment feasibility for educational institutions, supporting thousands of concurrent users while maintaining high prediction accuracy and user engagement through gamified learning paths
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