A Novel Placement Prediction System

Abstract

Predicting the placement outcomes of students is a critical task for educational institutions and students themselves. This project will conduct a comprehensive performance analysis of various placement prediction models, aiming to enhance the accuracy and reliability of such placement predictions. In this project, we will analyze a dataset of student profiles, academic records, internships, Average marks of 10^th, 12^th/diploma and placement results, employing machine learning and data mining techniques. The model's predictive accuracy will be used across multiple algorithms, including logistic regression, decision trees, K-Nearest Neighbor, Support Vector Machine and random forest, to evaluate the study's performance and to help the final-year students for the placement. The results of this analysis offer valuable insights into the strengths and weaknesses of different prediction models. They can guide the development of more effective placement prediction systems, ultimately benefiting final-year students and educational institutions for student placement and professional growth.