International Journal of Scientific Research and Engineering Development

In today’s competitive job market, predicting student placement outcomes has become an important task for educational institutions aiming to improve their training strategies and placement success rates. This project focuses on developing a predictive model using Machine Learning (ML) and Data Science techniques to forecast whether a student is likely to be placed based on various academic, demographic, and skill-based attributes. The system utilizes historical student data, including factors such as academic performance, attendance, technical skills, communication abilities, internships, and extracurricular activities. Data preprocessing techniques such as handling missing values, normalization, and feature selection are applied to ensure data quality and improve model performance. Various machine learning algorithms, including Logistic Regression, Decision Trees, Random Forest, and Support Vector Machines, are implemented and compared to identify the most accurate model. The goal of this project is not only to predict placement outcomes but also to identify the key factors that influence employability. This helps institutions provide targeted training and guidance to students who may be at risk of not being placed. Additionally, the system offers insights that can assist students in understanding the skills and qualifications required to enhance their career prospects. The results demonstrate that machine learning models can effectively predict student placement with high accuracy when trained on relevant and well-processed data. This project highlights the potential of datadriven decision-making in the education sector and contributes toward improving placement strategies and student outcomes.

Mrs. M Vijaya lakshmi, Dharik Ajees M, David S, Hari Palani R, Hari Krishnan S,"Student Placement Prediction Using Machine Learning and Data Science" International Journal of Scientific Research and Engineering Development, V9(3): Page(89-93) May-June 2026. ISSN: 2581-7175. www.ijsred.com. Published by Scientific and Academic Research Publishing.