PathFinder: An AI-Driven Framework for Enhanced Career Decision Support Using Machine Learning

Abstract: Students sometimes encounter considerable pressures and uncertainty in the complex process of selecting professional decisions. This paper offers a brand-new career-based recommendation system that is intended to help students overcome these obstacles. Technology extracts unique traits that are essential for identifying appropriate career choices by analyzing students' competence ratings and profiles using machine learning algorithms. The model suggests career pathways that are less likely to lead to both personal and professional fulfillment in addition to predicting the most suitable job possibilities based on the individual's talents and interests. The system's goal is to increase students' chances of success in their chosen vocations by empowering them to make educated decisions through personalized suggestions. This method is useful in educational and career planning contexts because it constantly refines its suggestions by integrating adaptive learning processes and personalizing career assistance.

Keywords: Career Guidance Systems, Machine Learning, Predictive Analytics, Support Vector Machine (SVM), Random Forest Classifier, Decision Tree Classifier, XGBoost Classifier, Educational Decision Support, Career Path Recommendation, Data-Driven Career Planning

JEL Classification Number:  I21, I23, C45, C53, J24