Predictive Analytics in Student Placement Management System Leveraging: Machine Learning

Abstract

This research explores the application of predictive analytics in student placement management systems, leveraging machine learning to enhance decision-making and streamline recruitment processes. Traditional placement systems often rely on manual and subjective methods, which can be time-consuming and prone to bias. By integrating machine learning techniques, predictive models analyze historical data, academic performance, extracurricular achievements, and skill sets to forecast students' placement potential and identify suitable career paths.

¹ This study discusses the design and implementation of a predictive analytics framework, highlighting the effectiveness of algorithms such as Gradient Boosting (achieving 99.90% accuracy), Random Forest (99.75%), and Decision Tree (99.90%) for placement prediction. Other models, including Support Vector Machine (96.90%), Logistic Regression (94.70%), and K-Nearest Neighbors (94.30%), were also evaluated. The models were trained and tested on a dataset of student profiles and job descriptions from institutes records and student interviews. Key features influencing placement success were identified as aptitude test score, projects,internship,CGPA etc. The findings demonstrate how predictive analytics, particularly using ensemble methods, can significantly improve placement outcomes, enhance employer-student matching, and support data-driven decision-making for placement coordinators. This research underscores the transformative potential of machine learning in modernizing student placement management systems and shaping future employment trends.