In today's competitive job market, engineering students often face challenges in securing placements due to a mismatch between their skill sets and industry requirements. This research focuses on developing a machine learning-based framework for skill gap analysis and placement assistance of engineering students. By leveraging historical academic records, technical skills assessments, and placement outcomes data, various machine learning algorithms are applied to predict the likelihood of placement success and identify specific skill deficiencies. The system analyzes patterns in student performance and provides actionable recommendations to bridge skill gaps through targeted training programs. Experimental results demonstrate that supervised learning models such as Random Forest and Support Vector Machines (SVM) offer high accuracy in predicting placement eligibility. This study aims to assist academic institutions and placement cells in making data-driven decisions to enhance student employability and streamline the placement process.
Machine Learning, Skill Gap Analysis, Placement Assistance, Engineering Students, Predictive Analytics, Random Forest, Support Vector Machine, Employability Prediction, Data-Driven Decision Making, Academic Performance Analysis