## 3 Methodology  
This section outlines the methodology followed for the study. The study's methodology is divided into three main steps: data pre-processing, feature importance selection, and model training with evaluation. In the initial data pre-processing phase, the dataset undergoes cleaning, categorical feature encoding, standard scaling, and over-sampling. The second step focuses on feature engineering to identify the most impactful features for predicting student performance and qualifications. The final stage encompasses model training and evaluation. During data pre-processing, the dataset is meticulously cleaned and organised using Python 3.11.2, essential features are systematically chosen, and issues such as imbalance and biases are addressed. In the third step, predictive models for student  
\begin{table}
\begin{tabular}{c c c c} \hline
**Ref.** & **Purpose** & **Advantages** & **Disadvantages** \\ \hline \multirow{4}{*}{[1]} & Addresses the concern of student & Provides insights into the factors & Does not investigate the relationship between emotional factors and academic performance in voluntary withdrawals. \\  & retention in engineering and built & influencing student persistence and attrition in higher education. & Lack of investigations on the factors affecting students’ achievements in particular courses within the Malaysian context \\  & environment programmes. & & \\ \hline \multirow{4}{*}{[23]} & To enhance students’ success and & We have identified the most & Lack of investigations on the factors affecting students’ achievements in particular courses within the Malaysian context \\  & benefit educators and academic & suitable prediction methods for predicting students’ performance. & Lack of investigations on the factors affecting students’ achievements in particular courses within the Malaysian context \\  & institutions. & & \\ \hline \multirow{4}{*}{[31]} & Proposes an imbalance learning & \multirow{4}{*}{Addresses the issue of imbalanced} & Does not explicitly mention any \\  & algorithm that uses Synthetic Minority & distributions, leading to more & disadvantages or limitations of the \\  & Over-sampling Technique (SMOTE) to & precise fault diagnostics and & algorithm or the research approach \\  & augment minority faulty classes and to & prognostics in industrial systems. & used. \\  & address the imbalanced class learning & & \\  & problem. & & \\ \hline \multirow{4}{*}{[32]} & Investigates the accuracy of SMOTE in & Provides a comprehensive & \\  & replicating the original data & theoretical analysis of the popular & There’s a point where bias becomes \\  & distribution and evaluates its impact on & over-sampling method, SMOTE, & more critical than variance, indicating \\  & classification boundaries and overall & which needs to be improved in the & the need to stop generating more data. \\  & performance. & literature. & \\ \hline \multirow{4}{*}{[40]} & Employs the K-means clustering & \multirow{4}{*}{Aids students and instructors in} & Needs to provide specific information \\  & algorithm to classify students into & pinpointing areas for improvement. & about the dataset size used to analyse \\  & performance-based clusters. & & students’ performance. \\ \hline \multirow{4}{*}{[41]} & Introduces an automated student & \multirow{4}{*}{Employs an automated data} & Does not fully implement automation, \\  & performance analyser and & visualiser to illustrate students’ & with room for improvements, such as \\  & recommender system using prediction & performance, showcasing their & automating processes like attendance \\  & algorithms and content-based & strengths and weaknesses. & checking. \\  & recommendations. & & \\ \hline \end{tabular}
\end{table}
Table 1: Summary of studies in the field.  
performance are trained using machine learning techniques. Models are then assessed based on selected features, employing performance metrics to measure accuracy and effectiveness in predicting student outcomes. Figure 1 depicts the methodology we followed in the study.