Smart College Analytics for Placement Readiness Prediction Using Machine Learning

The increasing demand for employability-ready graduates has exposed the limitations of traditional college place- ment systems, which rely primarily on academic performance and manual evaluation. These methods often fail to provide comprehensive student assessment, timely skill gap identification, and data-driven decision-making. This paper proposes a Smart College Analytics and Placement Readiness Support System using Machine Learning. The system transforms raw student data into intelligent employability profiles by integrating academic, technical, and skill-based information. Machine learning models are used to predict placement readiness, identify skill gaps, and classify students into different readiness levels. Additionally, the system supports early career planning by analyzing historical placement trends and job roles. It also provides analytics-driven insights for placement cells through interactive dashboards, enabling efficient candidate shortlisting and training strategies. The proposed system enhances placement outcomes by enabling proactive student development and informed institutional deci- sions.