Intelligent Career Prediction and Course Recommendation

Constructing a successful academic and career path is crucial for students, and informed decision-making during schooling significantly impacts their future prospects. This paper presents a machine learning-based recommendation system designed to assist students in selecting suitable subjects and career paths by analyzing various attributes such as demographics, academic performance, interests, and extracurricular activities. Utilizing a Random Forest Classifier, the model is trained on historical student data to predict personalized academic recommendations. The system incorporates data preprocessing techniques including label encoding and feature selection to optimize model performance. Evaluated on accuracy and classification metrics, the model demonstrates effective prediction capabilities. Additionally, the system features interactive roles for administrators and users, facilitating course management and personalized guidance. This intelligent recommendation framework aims to empower students, parents, and educators with data-driven insights, thereby enhancing educational and career decision-making processes for long-term success.