# Fair and Transparent Student Admission Prediction Using Machine Learning Models
## 2 Literature Review
### Student Admissions Through Machine Learning Models  
In their study, [PERSON] et al. [14] utilized the publicly available \"Graduate Admissions 2\" dataset from Kaggle to develop an automated model for predicting postgraduate admission likelihood. The primary objective of this research was to assist prospective graduate students in identifying and targeting universities that align optimally with their academic profiles. The same dataset was employed by [PERSON] et al. (2018), with the distinction that these authors implemented a Deep Neural Network model for their analysis. The publicly available dataset titled \"Recommendation System for Graduate Level Universities in the United States\" was utilized by [PERSON] et al. (2018). In their study, the authors proposed a stacked ensemble model that demonstrated superior predictive performance compared to other machine learning models, including deep learning approaches, in estimating a student's likelihood of admission to specific universities. The study by [PERSON] and [PERSON] (2017) examined the influence of selection criteria on the admission outcomes of non-native English-speaking MBA applicants in China, specifically within the Shanghai International MBA Program. The findings indicate that traditional selection metrics may inadequately assess the essential skills required for success in programs conducted in a non-native language. Consequently, the authors advocate for incorporating machine learning techniques into the student selection process to enhance admissions accuracy and identify candidates with a higher potential for success. The study by [PERSON] et al. (2018) examined university admissions criteria in Saudi Arabia, with an emphasis on enhancing the process of student major selection through machine learning methodologies. The research analyzed factors such as high school GPA, General Aptitude Test (GAT) scores, and Academic Test (AT) scores, applying models including K-Nearest Neighbor (KNN), Decision Tree (DT), and Support Vector Machine (SVM) to classify students into appropriate academic majors. [PERSON] et al. (2019) investigated improvements in student admissions processes at Saudi Arabian universities by integrating data mining and machine learning techniques. The study critiqued traditional admissions metrics, such as GPA and general aptitude scores, noting that they often fail to match students with the most suitable majors, resulting in higher dropout rates. To address this, the authors introduced an adapted Jaccard similarity model that links high school course performance with success in specific fields of study. Their findings suggest that this data-driven, personalized admissions approach could better align students' skills with appropriate academic tracks. In their study, [PERSON] et al. (2019) investigated the use of machine learning models to predict the likelihood of graduate admissions for applicants to U.S. universities. By training the model on key admissions criteria, such as GRE and TOEFL scores, undergraduate GPA, and research experience, their approach offers students an automated tool to assess their admission prospects. This method seeks to minimize dependence on potentially biased human consultants and to facilitate a more efficient application decision-making process. In their study, [PERSON] and [PERSON] (2018) examined predictive analytics for graduate admissions in engineering programs at Politehnica University Timisoara, Romania. The authors developed a multi-class classification model to predict student admission outcomes based on candidates' undergraduate grades, preferences, and demographic data. [PERSON] and [PERSON] (2018) proposed a machine learning-based approach to predict likelihood for postgraduate admissions for Indian students. Additionally, they deployed their model as an interactive R Shiny web application, providing a user-friendly interface that enables students to easily estimate their admission prospects. The study by [PERSON] et al.