Multiclass Prediction Model for Student Grade Prediction Using Machine Learning

Today, predictive analytics applications became an urgent desire in higher educational institutions. Predictive used advanced that encompasses machine learning implementation to derive high-quality performance and meaningful information for all education levels. Mostly know student grade is one of the key indicators can help educators monitor their academic performance. During past decade, researchers have proposed many variants techniques domains. However, there are severe challenges handling imbalanced datasets enhancing predicting grades. Therefore, this paper presents a comprehensive analysis predict final grades first semester courses by improving accuracy. Two modules will be highlighted paper. First, we compare accuracy six well-known namely Decision Tree (J48), Support Vector Machine (SVM), Naïve Bayes (NB), K-Nearest Neighbor (kNN), Logistic Regression (LR) Random Forest (RF) using 1282 real student's course dataset. Second, multiclass prediction model reduce overfitting misclassification results caused multi-classification based on oversampling Synthetic Minority Oversampling Technique (SMOTE) with two features selection methods. The obtained show integrates RF give significant improvement highest f-measure 99.5%. This indicates comparable promising enhance prediction.