Data-Driven Prediction of Campus Placement Success using Supervised Machine Learning Techniques

Predicting campus placement victory is a critical trial in connecting the gap between academic achievements and qualified opportunities. This paper explores the application of machine learning techniques to examine a dataset including academic performance, grade specialization, work experience, and other characteristics to predict placement consequences. Five models were implemented: Logistic Regression (accuracy: 76.92%), Decision Tree (accuracy: 96.92%), Random Forest (accuracy: 89.23%), K-Nearest Neighbors (accuracy: 96.92%), and Naive Bayes, which realized a perfect accuracy of 100%. These models efficiently recognized key factors manipulating settlement success and providing criminal visions for students and institutions. By leveraging data-driven methods, this research highlights the possible of machine learning to improve campus recruitment approaches, improve skill development, and align informative consequences with occupational demands.