## 3 Materials and Methods
Data Completeness: We assume that the datasets from educational institutions are complete and accurately reflect student performance and demographics. This is crucial as the model's accuracy depends significantly on the quality of input data. In practice, we mitigate the risk of incomplete data by applying data imputation techniques and liaising with educational institutions to understand and fill gaps in data collection processes. 2. General Education Framework: The model presupposes a relatively uniform educational structure within regions being analyzed. This assumption allows us to generalize the predictive factors across different institutions within the same educational system. We validate this assumption through a preliminary analysis of educational systems and curricula before model deployment. 3. Consistency in Course Impact: We posit that certain courses have a more pronounced impact on student attrition rates across different institutions. This is based on historical data showing consistent patterns of student performance in key subjects that correlate with dropout rates. To ensure this assumption holds, we continuously update and recalibrate our model as new data becomes available, ensuring it reflects the most current educational trends. 4. Student Behavior Consistency: The model assumes that student behavior and its impacts on attrition are consistent over time. While this may not capture new emerging trends immediately, the model includes mechanisms for periodic reassessment to integrate new behavioral patterns and external factors affecting student engagement and success. 5. Socioeconomic Factors: It is assumed that socioeconomic factors influencing student attrition are similar within the data sample. This assumption allows us to apply the model across similar demographic groups but requires careful consideration when applying the model to regions with differing socioeconomic landscapes. We justify this by conducting localized studies to understand the socioeconomic dynamics before applying the model in a new region. Python was chosen to run the machine learning process and build the predictive model, utilizing a random forest model for this simulation due to its extensive packages and libraries such as Scikit-Learn that makes it easy to be used [38; 39; 40; 41]. This model is known for its high prediction accuracy, ability to handle large datasets with many features, and robustness against missing values and outliers. It employs ensemble approaches, combining results from multiple algorithms to generate final predictions, which helps prevent overfitting and enhances prediction accuracy. As depicted in Figure 1, the random forest model is less sensitive to the choice of hyperparameters and can implicitly select features. The random forest model operates by creating multiple decision trees and combining their results to improve prediction accuracy. Each training dataset is provided with a subset of features and a random sample of observations to create diverse individual decision trees. Once these trees are built, they are used to make predictions on the test data. Each decision tree generates its own prediction, and the final prediction is determined by the majority vote of all the decision trees. The predictions are then evaluated using various metrics, such as accuracy, precision, and F1-score. Here, we detail the technical and methodological framework utilized in our study to predict student dropout rates using a machine learning model. The framework outlines the computational tools and data preprocessing steps essential for the effective implementation of the random forest classifier.