Student Performance Evaluation Using Machine Learning Algorithms

Abstract

The main perpose of the model is to predict the student performance in the final examination based on the previous records of the students. The Naïve Bayes and ID3 algorithms are trained using the training data set. The training inputs for the algorithm are directly taken from the stored file. After the algorithm is trained, then the inputs from the testing file are fed into the algorithm for prediction of the final result one by one. All the records are maintained in the stored file. In order to provide the input to the algorithm, an output window has been developed. The output window has been Coded, Developed and Designed in Net Beans. Each record from the training set is fed into the GUI interface one by one and then its output result is predicted and noted as either pass or fail. The process is repeated for all testing record set.