Predictive Models for Career Guidance, Based on Academic and Behavioral Data with Data Mining and Machine Learning Technique

A person's future is greatly influenced by career counseling, especially for students making decisions about their education and careers. This project focuses on using data mining and machine learning approaches to leverage behavioral and academic data to create prediction models for career advising. To produce individualized career suggestions, the suggested system combines data gathering, preprocessing, feature engineering, and predictive analytics. Academic performance indicators (e.g., grades, test scores), behavioral characteristics (e.g., learning styles, personality tests), and demographic information are important sources of data. While clustering techniques like K-Means make it easier to group students with similar profiles, machine learning algorithms like Random Forest, Support Vector Machines, and Neural Networks are used for classification. Furthermore, hybrid models that combine clustering and classification improve the accuracy and customization of the system. Metrics including precision, recall, F1 score, and clustering validation indices are used to evaluate the system, guaranteeing solid and trustworthy results. By assigning students to appropriate career groupings and recommending certain positions, the prediction algorithms offer useful insights. This study fills in the gaps in conventional career counseling approaches and opens the door for scalable, effective, and customized career advice systems by showing how cutting-edge data-driven approaches can enable students to make well-informed career decisions.