# Fair and Transparent Student Admission Prediction Using Machine Learning Models
## 3 Materials and Methods
### MBA Admission Dataset Description  
The dataset used in the study comprises synthetic data generated from the Wharton Class of 2025's statistics. The dataset was obtained in a CSV format from the Kaggle website [35]. The dataset consists of 6194 cases (number of applicants) and eight input feature attributes while the output feature refers to the result of the admissions process:* Gender: Applicant's gender (Male, Female);
* International: International student (TRUE/FALSE);
* GPA: Grade Point Average of the applicant (on 4.0 scale);
* Major: Undergraduate major (Business, STEM, Humanities);
* Race: Racial background of the applicant (e.g., White, Black, Asian, Hispanic, Other/ null: international student);
* GMAT: GMAT score of the applicant (maximum 800 points);
* Work_exp: Number of years of work experience;
* Work_industry: Industry of the applicant's previous work experience (e.g., Consulting, Finance, or Technology);
* Admission: Admission status (Admit, Waiting List, Deny).  
All of these admission criteria are fully aligned with those reported in research papers reviewed in [36]. From the above features, Gender, International, Major, Race, Work_industry, and Admission are considered to be categorical variables while the other features are considered to be numerical ones.  
The imbalance ratio for this dataset is calculated using the formula below:  
\[\text{Imbalance Ratio (IR)}=\frac{N_{\text{majority}}}{N_{\text{minority}}}=5.77\]  
where \(N_{\text{majority}}=N_{\text{Admit}}\) and \(N_{\text{minority}}\) represent the number of samples in the majority and minority classes, respectively. Although the current problem involves multi-class classification, we assumed that students placed on the Waiting List for admission are closer to a positive outcome than a negative one. We selected to remove all the cases related to the Waiting List, and therefore we defined \(N_{\text{minority}}=N_{\text{Deny}}\).  
Based on the descriptive statistics for the categorical features presented in Table 1 and the calculation of the imbalance ratio (see above), it can be inferred that the dataset is an imbalanced one, necessitating appropriate remedial techniques. For the Work_Industry feature, we could merge different categories to different groups but we selected to remove this feature for the subsequent experiment.  
Although the dataset is synthetic, the specific procedure or algorithm used to generate it remains unknown to us.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Feature** & **Category** & **Frequency** & **Percent (\%)** \\ \hline \multirow{2}{*}{Gender} & Male & 3943 & 63.66 \\  & Female & 2251 & 36.34 \\ \hline \multirow{2}{*}{International} & True & 1842 & 30 \\  & False & 4352 & 70 \\ \hline \multirow{2}{*}{Major} & Business & 2481 & 40.05 \\  & STEM & 1875 & 30.27 \\  & Humanities & 1838 & 29.67 \\ \hline \multirow{4}{*}{Race} & Asian & 1147 & 18.52 \\  & Black & 916 & 14.79 \\ \cline{1-1}  & Hispanic & 596 & 9.62 \\ \cline{1-1}  & White & 1456 & 23.51 \\ \cline{1-1}  & Other & 2079 & 33.56 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Descriptive statistics of categorical features.