## 4 Findings and Results
### Supervised Machine Learning Models
Specificity measures the proportion of actual dropouts that are correctly identified by the model. Specificity values ranged from 0.923 for logistic regression and probit regression to 0.956 for naive Bayes, decision tree, and SVM, respectively. A higher specificity indicates better performance in identifying students who are likely to drop out, as depicted in the specificity figure. This metric is essential for understanding how well the model can discern between students who will drop out and those who will complete their courses, thus aiding targeted intervention strategies. PPV or precision represents the proportion of correctly predicted completions. Most models had a PPV of approximately 0.993, except for logistic regression and probit regression, both at 0.988. This high PPV signifies that when a model predicts that a student will complete the task, it is very likely to be correct. High precision ensures that resources are not wasted on false positives, thereby enhancing the efficiency of the support systems. NPV is the proportion of predicted dropouts that are accurate. Here, the values range from 0.896 for KNN to 0.956 for the decision tree and SVM. The lower NPV for KNN indicates that it is less reliable in predicting dropouts compared to the decision tree and SVM, which maintain a high accuracy. Understanding NPV helps evaluate how well the model predicts students who are actually at risk of dropping out, ensuring that necessary interventions are directed appropriately. Prevalence indicates the proportion of the dataset that comprises actual completions, whereas the detection rate measures the proportion of actual completions detected by the model. All models showed a similar prevalence, around 0.870, and detection rates were slightly lower, reflecting their effectiveness in identifying completions. These metrics aid in understanding the baseline distribution of the dataset and the effectiveness of the models in maintaining this distribution in their predictions. Balanced accuracy, which is the average of sensitivity and specificity, provides a more comprehensive performance measure. The values range from 0.957 for logistic regression and probit regression to 0.975 for Decision tree and SVM, indicating that the latter models perform the best overall. A high balanced accuracy signifies that the model is not biased towards predicting either completions or dropouts, providing a balanced approach to prediction. The AUC measures the model's ability to distinguish between classes, with values above 0.970 indicating excellent performance. The AUC values for the models ranged from 0.971 for naive Bayes to 0.988 for random forest, demonstrating that all models had strong discriminative power, with random forest and SVM being particularly notable. A high AUC indicates that the model has a high capability to correctly classify students in terms of whether they will complete their courses or drop out. From a detailed analysis, it is clear that decision tree, SVM, and random forest are the top-performing models. They exhibited the highest accuracy, sensitivity, specificity, and AUC values, making them highly reliable for predicting student dropouts and completions. The high balanced accuracy of these models also suggests that they are well-calibrated and robust across different metrics. The detailed performance metrics in the figures underscore the effectiveness of these machine learning models in educational data mining. For educational institutions aiming to reduce dropout rates, implementing predictive models, such as decision trees, SVM, and random forest, can provide significant insights. These models can help identify at-risk students early, allowing for timely intervention and support systems to improve student retention rates. The high specificity and sensitivity of these models ensure that both completions and dropouts are accurately identified, thereby enhancing the overall effectiveness of predictive analytics in education.