## 1 Introduction
Moreover, this study attempts to mitigate the influence of human factors by utilizing data mining and machine learning methods. Furthermore, machine learning is applied in various fields, including education, healthcare services, marketing, and finance [11]. Machine learning models are heavily dependent on data and statistics. As a subset of artificial intelligence, machine learning can learn from data, recognize patterns, and make decisions with minimal human involvement. Indeed, there are two categories of machine learning models: supervised and unsupervised learning. Supervised learning involves providing the model with training examples to teach the algorithms how to generate the correct output. In unsupervised learning, algorithms can learn and discover hidden patterns on their own [12]. This research examines the admission standards for universities in Saudi Arabia, focusing on the impact of concealed information (which underlies students' GPAH, WS, and QS scores) on their selection of majors. First, a questionnaire is presented to experts to comprehend the ideal process of selecting a student's major. Second, we gather actual data about Jazan University students to examine the current state of the process of selecting a major. Here, we use data mining techniques to analyze the collected data and establish connections between student performance in the university, indicated by GPAU, and admission features such as GPAH, GAT, AT, WS, and QS. Actually, four types of relationship analysis are employed: modified Jaccard analysis, approximated modified Jaccard analysis, data distribution analysis, and correlation coefficient analysis. The research modifies Jaccard index [13] to suit the characteristics of the students' data records. Indeed, Jaccard analysis measures the similarity between two sets of data. The objective is to find the percentage of similarity between the elements in the sets. Our data sets include numerous attributes, including GPAU, GPAH, GAT, AT, WS, and QS. Each attribute is considered as a set, and the degree of similarity is calculated. However, since our data set includes students' records, we initially propose the notion of modified Jaccard analysis, which measures the similarity between the elements of two sets and of the same record. For instance, it examines the similarity between GPAU and GPAH for the same student. Next, we use approximated modified Jaccard analysis to measure similarity in a more lenient manner, treating values that are near each other as similar. Moving forward, we conduct an analysis of the data distribution, followed by using correlation coefficient analysis. Accordingly, the investigation shows that current admission procedure has a generalization issue, where the relationships tend to differ significantly from one major to another. Thirdly, this article suggests incorporating additional features, like the grades earned in high school courses, into supervised machine learning models to accurately find suitable majors. The paper specifically applies K-nearest neighbor (KNN), decision tree (DT), and support vector machine (SVM) algorithms to classify students into their respective major categories [14; 15; 16]. The experimental simulation revealed that KNN provides the most accurate results, with a 100% success rate. In comparison, the DT has an accuracy rate of 81% and the SVM has an accuracy rate of 75%. Employing this process has the potential to increase enrolment rates for students in majors that are best suited to their individual strengths and abilities. This enables students to graduate on time with a wealth of knowledge, practical experience, and overall satisfaction. Additionally, this technique can substantially reduce the frequency of students transferring, withdrawing, or failing courses.