Classification Model to Predict the University Major Using Decision Tree Algorithm
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Abstract
Data mining education has emerged as a powerful method for predicting students' academic success and uncovering hidden links in educational data. The goal of this paper is to create an intelligent prediction system to assist in guiding and counseling students who were preparing to start college. The main issue addresses in the study is that many students end up specializing in areas they do not desire, often influenced by the advice of friends or family. The performances of Decision Tree (ID3), Naïve Bayes (NB), and k-Nearest Neighbor (kNN) algorithms, among the data mining algorithms, are calculated and compared to predict the final appropriate university major for students. This involves analyzing the data and evaluating the training performance of classifiers in terms of accuracy, precision, sensitivity (recall), and F-measure. The study utilizes actual, private data to construct the system. After undergoing various processing procedures, the student data from Sebha Universities are organized into different categories and prepared for the algorithms to be trained on, with an accuracy rate of 64.28%, the Decision Tree method proves to be the most effective among the three classification algorithms that are tested.
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