Authors: S. Srinivas, A. Kalyani
Abstract: Campus placement prediction is a major use of machine learning in the field of higher education. Because of this, schools may gauge a candidate's employability based on their grades and other skill-related attributes. By accurately predicting students' placement results, training and placement cells may better assist students in gaining a better understanding of their strengths and areas for improvement, which in turn helps students in their professional development. This research presents a machine learning-based predictive analytics platform for on-campus placements. The platform uses a variety of criteria, including students' technical ability, internship experience, project work, credentials, and academic records, to forecast how campus placements will go. To eliminate discrepancies, identify significant features, and understand the interrelationships of different variables, the collected student dataset is first subjected to data preparation and exploratory data analysis (EDA). Now we can go on to comparing and applying different supervised machine learning methods. Decision trees, logistic regression, K-nearest neighbours (KNN), and random forests are all examples of such methods. F1-Score, Accuracy, Precision, and Recall are typical performance indicators used in this assessment. Decision Tree and K-Nearest Neighbours classifiers get the highest prediction accuracy when evaluated on the selected dataset. Academic profile analysis and real-time placement predictions are both made possible via an interactive online interface that students and placement managers may utilise. Because of this, the suggested framework is more practically useful. One web program that uses Django is able to use the learned prediction model. This data-driven solution is scalable, effective, and designed to improve campus recruitment strategies, student career guidance, and institutional placement outcomes.
DOI: http://doi.org/10.5281/zenodo.21374561
