Comparative Analysis of ML Algorithms and Neural Networks to Classify Student Based on Academic Performance

In recent years, there has been an interesting surge in interest in both educational data and deep learning. This research proposes a Neural Network (NN) architecture that visually represents the class category to students. This gives information to the school so they can help the pupils who might otherwise fail. A comparative analysis between the suggested model and present-day techniques of machine learning that utilizes the identical dataset. Compared to existing machine learning algorithms, the accuracy of the suggested neural network model is higher.