The document presents a classification model that predicts students' knowledge level in a subject based on variables like study time, exam performance, and repetition. It uses a k-nearest neighbor algorithm and data from PhD students studying electrical machines. The model was 70% trained and 30% tested. It achieved 89% accuracy in predicting students as having high or low knowledge. The model can help recruitment and colleges assess student knowledge levels.