Comparative Analysis of Machine Learning Classification Algorithms and Hybrid Models for Student Performance Prediction

Ms. Pooja C. Soni, Dr. Hetal R. Modi, PC Negi

This study focuses on the analysis and comparison of machine learning classification algorithms and hybrid machine learning models for predicting student academic performance. Educational Data Mining techniques are used to extract meaningful insights from student datasets. Various classification algorithms such as Decision Tree, Naïve Bayes, Support Vector Machine, K-Nearest Neighbors, and Random Forest are applied and compared with hybrid models. The performance of these models is evaluated using accuracy, precision, recall, and F1-score. The study aims to identify the most efficient model for early prediction of student performance. This research focuses on predicting student grades (A–F) using a hybrid machine learning framework that combines academic scores, attendance records, and behavioral data. By using school-level datasets, the study ensures that the approach is practical and relevant to real-world education. The model applies classification techniques with structured data to improve accuracy and reliability. To measure performance, a confusion matrix and evaluation metrics like accuracy, precision, recall, and F1-score are used. Results show that most predictions match the actual grades, with strong diagonal values in the matrix, meaning the model is highly accurate across all grade categories. The study also includes Explainability methods to highlight which features matter most. Findings reveal that exam scores, attendance, and behavior strongly influence predictions. Compared to single-model approaches, the hybrid method is more stable and reduces errors. Overall, this work contributes to building smart educational systems that are scalable, interpretable, and useful for teachers. By identifying student performance levels early, educators can take timely action, provide personalized support, and improve learning outcomes.