## 2 Literature Review
The researchers in [20] introduced an adaptive system to recommend a suitable engineering department, based on the preparatory year grades and the final grades upon completing the program, by training multiple ML such as SVM, linear regression (LR), and RF. The RF reached an accuracy of 82.57%. Other researchers, such as in [10], introduced an expert system to assist high school students in selecting their university program using the DTC algorithm, which simplifies the decision-making process by breaking it down into a series of simpler decisions, making the solution easy to understand. This system was able to achieve an accuracy of 98%. Some researchers follow a hybrid or hierarchal model that combines two or more algorithms. The study [21] used a hybrid model that combined the multi-class SVM and KNN algorithms, where the SVM classified the graduate schools that are likely interesting to a candidate, and the KNN algorithm classified universities and colleges, using the same skills and prerequisites. Similarly, [21] adopted a hybrid model that combined both Knowledge Base (KB) and Collaborative Filtering to help students in choosing their university, university majors, and job options; the CF was used to calculate student scores and generate recommendations based on similarities. Then, the outcome of the CF was input into the KB recommendation system to recommend personalized suggestions based on a student's demographic and academic history. The study [22] utilized data on academic results, personality, and intelligence to select the appropriate major using a hierarchal classification approach; the first classifier was responsible for predicting the main streams, and another classifier (for each stream) predicted the subcategory of the major. Each classifier in this hierarchal model was trained using two classification algorithms, Random Forest and Multi-Layer Perceptron, in addition to 10-fold cross-validation to confirm the classification accuracy, which ranged from 89.29% to 96.10% using the RF and confirmed that the hierarchal model outperformed the flat one. Anther hierarchal model was implemented in [23] using Multi-level SVM to categorize a graduate school; a KNN algorithm was used to generate graduate programs with comparable prerequisites and qualifications, with an accuracy of 58%. Deep Learning Algorithms have also been used in this field. For instance, in [24], the Deep Neural Network algorithm along with five other ML classification algorithms, including LR, SVM, KNN, RF, DT, and Gaussian Naive Bayes, were used for university admission systems. The results show that the Deep Neural Network algorithm was the best, with an accuracy of 95.1%. Other researchers [25] have compared the Neural Network against Nearest Neighbor and Decision Tree; the former was able to achieve 71.30%. Despite the existence of these related research works, there remain several challenges. For instance, in some research [15], the major prediction is decided based on the student's score at high school, without considering other factors such as the job market, academic history, etc. Many existing studies achieved only a low classification rate, e.g., [1, 12, 13, 26]. Other research works have achieved high classification results; however, they depend on a complex workflow of preprocessing the data. For instance, the authors in [14] initially prepared the data by applying a certain filtration, discretization process, and binarization for the features and even applied data augmentation techniques to increase the size of the data [21]. Furthermore, some researchers have used sophisticated classification algorithms to improve the accuracy rate. For example, in [19], a hybrid model was built, in [15, 18, 23] a hierarchical model was adopted, and in [17] the implemented system was based on using big data technology concepts such as Hadoop and MapReduce. Finally, not all researchers consider the hyper-parameter optimization for the ML, for instance, [3].