## 5 Discussion, Conclusions, and Recommendations
### Discussion  
This study employed various machine learning techniques, such as logistic regression, probit regression, naive Bayes, random forest, decision tree, support vector machine (SVM), and K-nearest neighbors (KNN), to forecast student attrition rates. Every model offers distinct perspectives on the elements that contribute to dropout rates. Demographicvariables such as age and sex had a substantial impact. Younger students, particularly those going through important transitional periods, exhibited elevated dropout rates, indicating the need for interventions tailored to their unique age groups. Observations were made regarding disparities in dropout rates based on sex, emphasizing the necessity of measures that consider sex-specific considerations.  
As an educational indicator, grades repeatedly proved to be the most influential predictor in several models, including naive Bayes and random forest. This implies that the individual difficulties encountered at each grade level may be a significant contributing factor to students leaving school before completion. Socioeconomic factors, such as the level of money within a home and the number of individuals residing in the household, also had an influence. Lower levels of household income were linked to higher rates of students leaving school before completing their education, showing that economic obstacles hinder access to education. Similarly, larger household sizes were found to be related to higher dropout rates, suggesting that larger families have limited access to the resources necessary for education [38].  
However, the random forest model consistently outperformed the other algorithms, achieving the highest AUROC score and demonstrating the greatest accuracy in predicting student dropout. This exceptional performance highlights random forest as the most accurate and reliable model for predicting student attrition [23].  
The performance of the models varied, with the SVM and decision tree models demonstrating higher performances in terms of high accuracy and F1 scores. This indicates that these models are dependable in predicting student dropout. The random forest model performed exceptionally well, achieving the highest AUROC score, indicating its excellent ability to discriminate between different classes. Although the AUROC was significantly lower, the naive Bayes and KNN models demonstrated high accuracy and sensitivity, further supporting their usefulness in classification applications. The prevalence of the \"Grade\" attribute in several models gives rise to worries of overfitting, thus requiring meticulous validation [19]. This study highlights the importance of achieving a well-balanced feature engineering approach to enhance the generalization ability of models.  
In addressing the potential concern of overfitting in our study, we took several precautionary measures to ensure the reliability and generalizability of our predictive models for school dropout in Somaliland. To mitigate overfitting, we carefully evaluated our models using a variety of performance metrics on both the training and validation datasets. Additionally, we employed methods such as hyperparameter tuning, model complexity constraints, and ensemble techniques. By fine-tuning hyperparameters, such as adjusting the maximum depth of decision trees in random forest or the number of neighbors in KNN, we aimed to strike a balance between model complexity and performance. Additionally, we restricted the complexity of SVM models by optimizing the regularization parameter and selecting appropriate kernels. For logistic, probit, and naive Bayes models, we focused on feature selection and model simplification to prevent overfitting and improve predictive accuracy. These strategies collectively aimed to promote model simplicity and robustness, guarding against overfitting while ensuring the models' effectiveness in predicting school dropout outcomes.