Machine Learning-Based Student Placement Prediction System Using Random Forest Classifier

Abstract

The Student Placement Prediction System is an AI-driven solution designed to forecast the likelihood of students securing campus placements using machine learning techniques. The system employs a Random Forest Classifier to analyze multiple factors, including academic performance, internships, technical skills, and extracurricular activities. By leveraging historical placement data, it generates accurate predictions and provides actionable insights for students and institutions. The platform is designed with a modular architecture incorporating data management, prediction, and analytics components, ensuring scalability and ease of use. Additionally, it emphasizes data privacy, transparency, and ethical practices. This intelligent system enhances decision-making, supports early career guidance, and improves overall placement outcomes by bridging the gap between student capabilities and industry requirements.