Published Paper Details:

Student career prediction play an important role in assessing employability trends and enhancing career guidance in schools. This study applies advanced machine learning techniques like Logistic Regression, D T, R.F, SVM, and Neural Networks to predict student placement results from academic records, technical competencies, soft competencies, internships, and extracurricular activities. The system improves accuracy using data-driven information rather than traditional, human-centered evaluation methods. By automating the process, it minimizes human intervention, eliminates mistakes, and gives fair and impartial appraisals, providing equal opportunities for all students. The models are validated with accuracy, precision, recall, and F1-score to determine the best approach, with Decision Tree model achieving a staggering 91.25% accuracy, 89.19% precision, 89.73% recall, and 89.46% F1-score. It also helps students in identifying areas that require improvement on their part so that they may improve their technical as well as soft skills and prepare themselves for the job. Its scalability and flexibility allow it to be easily applied in various institutions, courses, and departments. It closes the gap between academia and industry, and thus maximizes placement plans for students, reduces unemployment, and allows institutions to tailor training courses to enhance the employability of students.