## 3 Methodology
### Data Pre-Processing
#### 3.3.1 Data Cleaning  
Using real-world datasets in the educational setting may be biased because women are underrepresented in STEM. When the user's demographic information, such as gender, ethnicity, and age, is used in classification problems, this bias is proliferated in the classifications made [43]. To establish a just and inclusive algorithm, it is crucial to ensure that concerns of bias and ethics are excluded from the dataset used for building the predicting qualifications. Therefore, it was necessary to remove demographic characteristics from the dataset. Gender, birthdate, marital status, language, nationality grouping, ethnic group, international race, urban-rural and study address were removed. Table 3 describes the data types of the features contained in the dataset after cleaning. The elimination of demographic characteristics aids in reducing any potential biases that may result from elements like ethnicity or nationality. This ensures that predictions are only based on student's abilities and achievements, which reduces the possibility of perpetuating discriminatory practices.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Column** & **Description** & **Count** & **Data Type** \\ \hline Academic Year & Year of registration & 29,158 & Int8 \\ Student Number & Student’s unique identifier & 29,158 & Int16 \\ Qualification Flag & Type of qual (mainstream or extended) & 29,158 & Int8 \\ Qualification & The qualification enrolled for & 29,158 & Int8 \\ Result Groupings & Student’s results for the year & 29,158 & Int8 \\ Period of Study & Student’s year of study & 29,158 & Int8 \\ Gender & Student’s gender & 29,158 & Int8 \\ Birthdate & Student’s date of birth & 29,158 & Int16 \\ Marital Status & Student’s marital status & 29,158 & Int8 \\ Language & Student’s native language & 29,158 & Int8 \\ Nationality & Student’s country of birth & 29,158 & Int8 \\ Ethnic Group & Student’s ethnicity & 29,158 & Int8 \\ Race & Student’s race group & 29,158 & Int8 \\ APS & The student’s admission point score & 29,158 & Int8 \\ Urban-Rural & Student’s home location—urban or rural & 29,158 & Int8 \\ Study Address & Student’s residence & 29,158 & Int16 \\ Previous Activity & Student’s activity the previous year & 29,158 & Int8 \\ Matric Date Year & Year student completed matric & 29,158 & Int8 \\ Module Name & Name of module enrolled for & 29,158 & Int16 \\ Module Mark & Mark obtained from the module & 29,158 & Int8 \\ Mathematics & Mathematics mark in high school & 29,158 & Int8 \\ Physical Sciences & Science mark in high school & 29,158 & Int8 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Variables and their distribution in the initial dataset.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Feature** & **Type** & **Classes/Value Range** \\ \hline Academic year & Categorical & 2 (2016, 2017) \\ Qualification flag & Categorical & 2 (mainstream, extended) \\ Qualification & Categorical & 8 (list of qualifications) \\ Result Grouping & Categorical & 5 (obtained qualification, continuing with studies, no/slow progress, no result, no readmission) \\ Period of study & Numerical & 4 (1, 2, 3, 4) \\ APS & Numerical & 18–46 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Variables Retained in the Dataset.