Enhancing Career Pathways: Advancing Guidance Systems with XGBoost Algorithm

This research study introduces a comprehensive framework tailored to navigate the complexities of contemporary career guidance. It integrates interconnected modules, including secure login, student data collection, academic evaluation tools, and an advanced skill assessment module. Notably, the Cognitive Decision Support module employs advanced analytical tools to analyze users' cognitive responses, delivering personalized career suggestions. Moreover, the framework features a career prediction module equipped with algorithmic models for forecasting career paths and suggesting suitable colleges nationwide. The dynamic skill evaluation component continually monitors industry trends, providing guidance for ongoing skill development. Additionally, it offers insights into the global job market and cultural trends, facilitating international career exploration. A feedback mechanism ensures continuous improvement. Despite its technical sophistication, challenges arise in managing largescale data processing and ensuring precise cognitive assessments, potentially affecting the accuracy of personalized career recommendations.