## 4 Findings and Results
### Model Evaluation  
Assessing classification models that utilize diverse performance indicators is crucial for understanding their efficacy in forecasting student attrition rates. The F1 score is a crucial indicator that combines precision and recall and provides a thorough assessment of a model's accuracy. The support vector machine (SVM) and decision tree models exhibited outstanding performance in this investigation, with F1 scores of 0.993. A high score demonstrated the competency of these models in properly predicting both dropouts and students who will continue their education. The elevated F1 score of these models highlights their resilience in addressing the intricacies of dropout prediction. Sensitivity, also known as recall, is a crucial parameter that quantifies the percentage of true dropouts successfully detected by the model. Ensuring high sensitivity in educational environments is essential for promptly identifying all at-risk children and providing timely interventions. In this study, the decision tree, SVM, and random forest models demonstrated a notable sensitivity value of 0.993, indicating their great efficacy in identifying students who are likely to discontinue their education. The high sensitivity of this method permits precise targeting of treatments, which may lead to a large improvement in student  
Figure 4: Model comparisons. retention rates. The capacity to accurately identify almost all students who are at risk of discontinuing their education makes these models exceptionally beneficial for educational authorities aiming to minimize dropout rates. Precision is a crucial metric that quantifies the ratio of accurate positive predictions to all the positive predictions generated by the model. Ensuring high precision prevents the allocation of resources to false positives; that is, students who are not genuinely at risk of dropping out. The logistic and probit regression models produced accuracy scores of approximately 0.988, indicating a high level of reliability of their predictions. The high level of precision guarantees that interventions are both effective and efficient, targeting only pupils who truly require assistance. The F1 score validates the performance of these models in real applications by demonstrating the balance between precision and recall. The AUROC (area under the receiver operating characteristic curve) is a metric that evaluates the performance of a model over all possible categorization criteria. A higher AUROC value signifies superior overall performance, demonstrating the capacity of the model to accurately differentiate between dropouts and non-dropouts. The RF model achieved an AUROC of 0.988, demonstrating its exceptional discriminatory capability. The high AUROC value indicates that the RF model is highly dependable in forecasting student dropout, which serves as a strong foundation for the development of early intervention programs. These measurements provided a comprehensive understanding of the strengths and limitations of each model. This ensures that the chosen model performs well both in theory and in practical real-world applications, effectively reducing the student dropout rates. A thorough assessment of various machine learning models emphasizes their diverse capabilities in forecasting student attrition rates. Models such as decision trees, support vector machines (SVMs), and random forests perform exceptionally well in metrics such as F1 score, sensitivity, and AUROC, showing their capacity to reliably detect children who are at risk while decreasing the occurrence of false positives. Logistic and probit regression models are known for their excellent accuracy and guarantee the efficient allocation of resources to students who truly require assistance. The comprehensive performance indicators offered by these models empower educational institutions to make well-informed decisions regarding their implementation, striking a balance between the requirement for precise predictions and practical considerations of intervention tactics.