Student Performance Prediction Model using Machine Learning Algorithms

Predicting student performance has become an increasingly vital aspect of educational research, providing educators with valuable insights for personalized learning interventions. This model is a comprehensive student performance prediction model using various machine learning algorithms like Logistic Regression, Naïve Bayes Classifier, SVM and K-Nearest Neighbors. The developed model not only provides valuable insights for educators to identify at-risk students but also enables the implementation of targeted interventions to improve student outcomes. Furthermore, the model's flexibility allows for customization according to different educational contexts, making it a valuable tool for educational institutions striving to enhance student success and retention rates. When compared to the results of the most recent research done on this subject, the proposed model provides a better result in terms of accuracy of the model.