A Comparative Analysis of Prediction of Student Results Using Decision Trees and Random Forest

Abstract

Many types of research are based on students' past data for predicting their performance. A lot of data mining techniques for analyzing the data have been used so far. This research project predicts the higher secondary students' results based on their academic background, family details, and previous examination results using three decision tree algorithms: ID3, C4.5 (J48), and CART (Classification and Regression Tree) with other classification algorithms: Random Forest (RF), K-nearest Neighbors (KNN), Support Vector Machine (SVM) and Artificial Neural Network (ANN). The research project analyzes the performance and accuracy based on the results obtained. It also identifies some common differences based on achieved output and previous research work.