## 4 Findings and Results
### Features Selection  
These features highlight the critical role of socioeconomic factors in influencing student dropout rates in Somaliland. The graphical representation of household income levels suggests that family financial background is a significant predictor of whether a student continues their education or leaves the schooling system prematurely. This finding aligns with the robust body of research that has consistently demonstrated the impact of poverty and resource constraints on educational outcomes, particularly in developing contexts, such as Somaliland. Figure 3 shows the importance of various features in a logistic regression model, with each bar representing the significance of a feature. Age emerged as the most crucial predictor, followed closely by grade, indicating that these two features played the most significant roles in the model's outcomes. Sex also shows notable importance, whereas school type, residence, and household size (HHSize) contribute to a lesser extent. Features such as Typehousing, Occupation, District, and Region have minimal to negligible impacts on the model's predictions, with Region being the least influential. This analysis highlights that demographic and educational factors are the primary drivers of the model predictions. Figure 3: LR feature selection results from the competitive models, including LR, PR, KNN, and RF algorithms. Figure 3 illustrates the importance of various features in a probit regression model, revealing that age and grade are the most significant predictors, each with an importance score of just above 0.6. Sex also plays a notable role, with a score of approximately 0.25, while household size contributes moderately, with a score of about 0.1. School type, type of housing, and occupation had minimal impact, with scores ranging from approximately 0.05 to just below 0.1. Residence, district, and region had negligible influence, with importance scores close to 0. This analysis underscores that demographic and educational factors are the primary drivers in the model, whereas geographic and housing characteristics have minimal effects. Figure 3 demonstrates the feature importance of various variables in a random forest model, revealing that \"Grade\" is overwhelmingly the most influential feature, dwarfing all others such as \"School type\", \"Region\", \"Household size\", \"Age\", \"Residence\", \"District\", \"Sex\", \"Typehousing\", and \"Occupation\". This suggests that the model's predictive power is primarily derived from the \"Grade\" feature, with other variables contributing minimally. This heavy reliance on a single feature indicates a need to validate the model to ensure that it is not overfitting and to consider additional feature engineering or transformation to enhance the importance of other variables, align the model's behavior more closely with domain knowledge, and reduce potential biases. The KNN model as shown in Figure 3 relies predominantly on \"Grade\", followed by \"Age\" and \"Schooltype\". The remaining features contribute little to the decision-making process of the model. This suggests that while \"Grade\" is the primary driver of the model's predictions, \"Age\" and \"Schooltype\" also provide some additional predictive power, albeit to a much lesser extent. As with the random forest model, it is important to validate the KNN model to ensure that the reliance on \"Grade\" is justified and to consider ways to enhance the significance of other features if necessary. As with the random forest and KNN models, this strong reliance on a single feature necessitates careful validation to ensure that the model is not overfitting, and that the dependence on \"Grade\" is justified. In addition, it may be beneficial to explore feature engineering or selection techniques to enhance the importance of other variables and provide a more balanced model.