AI-Based Prediction of Student Placement Readiness: A Machine Learning

This paper proposes an AI-based student placement-readiness prediction system using machine learning techniques to assess employability-related skills and identify areas for improvement. Student placement readiness depends on several factors, including academic performance, aptitude, technical skills, communication abilities, project experience, internships, and interview preparation. The proposed system integrates data validation, preprocessing, feature engineering, machine learning prediction, explainability, and personalized skill-gap recommendations. Candidate algorithms such as Logistic Regression, Decision Trees, and Random Forests can be evaluated using appropriate metrics, including precision, recall, macro-F1 score, and calibration. The framework aims to help students understand their preparation levels and receive actionable recommendations for improving their skills. It may also support placement coordinators in planning targeted training activities. The system is intended as a decision-support tool rather than a guarantee of employment or an automated method for denying placement opportunities. Privacy, fairness, explainability, secure data handling, and human oversight are important design considerations. This paper presents a proposed framework; no experimental results are claimed. Actual performance must be evaluated using an authorized, representative dataset.