USING MACHINE LEARNING FOR CAMPUS PLACEMENT PREDICTION

USING MACHINE LEARNING FOR CAMPUS PLACEMENT PREDICTION

Neelamani samal, Nandini Bhardwaj, Abhishek Chamoli, Rahul Dey, Yuvjeet

Department of Computer Science and  Enginnering
Chandigrah University
Mohali, Punjab

Abstract— In the modern world, campus placements play a crucial role in shaping the career trajectories of students and determining the reputation of educational institutions. This study addresses the growing need for accurate and predictive tools in campus placement through the application of machine learning (ML) models. We focus on widely-used ML algorithms that have emerged as powerful tools for predicting fitting technical fields. The research utilizes a comprehensive dataset that incorporates diverse features, including academic performance for technical proficiency. By leveraging these features, this project explores the application of ML algorithms to predict suitable job profiles. By analyzing the academic and technical performance of student's data and identifying relevant features, ML models can learn patterns and relationships that contribute to predict the placement potential of current students, guiding them towards career paths and skill development that align with their strengths and interests.

   Keywords— Machine Learning, Campus Placement, Random Forest Classification, Logistic Regression, Decision Tree, K-Nearest Neighbour Gradient Boosting Classifier, Predictive Analytics.