Stacked Ranking Feature Cluster Machine Learning (Srfcml): A Novel Method of Career Planning of College Students Based on Career Interest Assessment and Machine Learning

Career interest assessment, powered by machine learning algorithms, revolutionizes the way individuals explore and align with career paths. By analyzing vast datasets encompassing factors such as skills, preferences, personality traits, and job market trends, machine learning models can provide personalized career recommendations tailored to individual strengths and aspirations. These algorithms leverage advanced techniques such as natural language processing (NLP) to interpret self-assessment responses and match them with suitable career options. Additionally, machine learning algorithms continuously refine their recommendations based on user feedback and real-world outcomes, ensuring accuracy and relevance over time. This paper presents a novel approach to career planning for college students, integrating career interest assessment with machine learning techniques, specifically utilizing the Stacked Ranking Feature Cluster Machine Learning (SRFcML) model. The proposed framework leverages large-scale datasets encompassing diverse factors such as academic performance, skills, interests, and industry trends to provide personalized career recommendations. Through the application of m