Prediction of Students Final Project Values Based on Errors in Scientific Writing Using Data Mining Classification Algorithms

College databases are quickly amassing more and more data. Some data, which has not been used to raise the caliber of student performance, includes secret information regarding student performance. Data mining is used to examine accessible educational data and reveal any hidden information that may be there. This study uses a data mining classification model to develop rules that can forecast the final project value of program students' information management based on course values that aid in the final project's preparation. The study's analysis of student performance in courses that help them prepare for their final projects will also be included. According to the subject that underpins their final project, this prediction is supposed to assist in determining grades. Predictive analysis employing ID3 has an accuracy of 65.54%, CHAID 70.67%, and Nave Bayes 73.13%, According to research that has been done, the competition from the Naive Bayes algorithm has succeeded in being slightly superior to other algorithms.