IMPROVING PERFORMANCE OF STUDENTS’ GRADE CLASSIFICATION MODEL USES NAÏVE BAYES GAUSSIAN TUNING MODEL AND FEATURE SELECTION

Student grades are a relevant variable for predicting student academic performance. In achieving good and quality performance, it is necessary to analyze or evaluate the factors that influence When educator can predict students' performance from start, adjust way of learning so run effectively. The purpose this research study how applied determine interrelationships between variables find out which have an effect, then use as feature selection technique. Then, researchers review most popular classifier, Gaussian Naïve Bayes (GNB). Next, we survey models discuss approach. study, will classify based on existing features guide educators in selecting methods assist students planning process. result applying (GNB) without has lower accuracy 10.12% while using increases 10.12%.