## 4 Findings and Results
### Supervised Machine Learning Models  
Table 4 presents a comprehensive analysis of various machine learning models used to predict student dropout and completion rates. The models evaluated included logistic regression, probit regression, naive Bayes, random forest, decision tree, support vector machine (SVM), and K-nearest neighbors (KNN). Each model's performance was assessed using several critical metrics: accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), prevalence, detection rate, detection prevalence, balanced accuracy, and area under the curve (AUC). Initially, the table delineated the predictions made by each model, distinguishing between correctly predicted completions and dropouts. For instance, logistic regression correctly predicted 773 completions and 7 dropouts, whereas probit regression demonstrated similar results with 773 completions and 7 dropouts. In contrast, naive Bayes showed 777 completions and 5 dropouts, indicating a slight improvement in correctly identifying the completions. These predictions are critical for understanding how each model performs in real-world scenarios, where accurate identification of both completions and dropouts is essential. Accuracy measures the proportion of correct predictions (both completions and dropouts) of the total predictions made. The accuracy values for the models ranged from 0.980 for KNN to 0.988 for decision tree and SVM, as shown in the accompanying figure. This indicates that most models are highly accurate, with the decision tree and SVM slightly outperforming the other models. High accuracy is a desirable attribute in predictive models as it directly correlates with the reliability of the predictions made by the model. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Legistic Regression**} & \multicolumn{2}{c}{**Probit Regression**} & \multicolumn{2}{c}{**Native Bayes**} & \multicolumn{2}{c}{**Random Forest**} & \multicolumn{2}{c}{**Decision Tree**} & \multicolumn{2}{c}{**SVM**} & \multicolumn{2}{c}{**KNN**} \\ \hline
**Predicted** & **Completed** & **Dropout** & **Completed** & **Dropout** & **Completed** & **Dropout** & **Completed** & **Dropout** & **Completed** & **Dropout** & **Completed** & **Dropout** & **Completed** & **Dropout** \\ \hline Completed & 773 & 9 & 773 & 777 & 74 & 774 & 774 & 8 & 777 & 5 & 777 & 5 & 776 & 6 \\ \hline Dropout & 7 & 109 & 7 & 7 & 5 & 5 & 5 & 111 & 5 & 111 & 12 & 104 \\ \hline Accuracy & 0.982 & 0.982 & 0.986 & 0.986 & 0.986 & 0.988 & 0.988 & 0.988 & 0.980 & 0.984 & \\ \hline Sensitivity & 0.981 & 0.981 & 0.981 & 0.981 & 0.980 & 0.980 & 0.980 & 0.984 & \\ \hline Specificity & 0.983 & 0.983 & 0.983 & 0.980 & 0.980 & 0.983 & 0.983 & 0.982 & \\ \hline Neg pred value & 0.989 & 0.989 & 0.989 & 0.986 & 0.986 & 0.986 & 0.986 & \\ \hline Prevalence & 0.988 & 0.988 & 0.983 & 0.987 & 0.980 & 0.980 & 0.987 & 0.987 & \\ \hline Detection rate & 0.880 & 0.880 & 0.886 & 0.881 & 0.885 & 0.886 & 0.884 & 0.884 & \\ \hline Detection prevalence & 0.870 & 0.870 & 0.870 & 0.870 & 0.870 & 0.870 & 0.870 & 0.870 & 0.870 \\ \hline Balanced accuracy & 0.987 & 0.987 & 0.987 & 0.983 & 0.983 & 0.9875 & 0.986 & 0.985 \\ \hline AUC & 0.985 & 0.984 & 0.971 & 0.988 & 0.985 & 0.986 & 0.983 & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: ML models. Figure 2: Magnitude of school dropout among adults in Somaliland based on NEAS 2022 dataset. The sensitivity, or recall, is the proportion of actual completions correctly identified by the model. All models exhibited high sensitivity, ranging from 0.984 for KNN to 0.993 for decision tree, SVM, and random forest. This consistency indicates that the models are effective in identifying students who will complete their courses, as illustrated in the sensitivity graph. High sensitivity is particularly crucial in educational contexts where accurately identifying students who are likely to succeed can help provide appropriate support to those at risk.