## 3 Research Methodology
### Classification Algorithm Selection  
Among several classification algorithms, the selection of DTs, NB, and k-NN for the selected dataset was conducted under the spectrum of their unique strengths. While DTs, NB, and k-NN are strong candidates for small and numeric datasets, alternative algorithms such as logistic regression, support vector machines (SVMs), and random forest might offer better performance depending on the specific data characteristics [13; 14; 22].  
Logistic regression performs exceptionally well if the relationship between data characteristics or features and the target is linear in small datasets. SVMs can well manage small numeric datasets when the classes are clearly separable. Random forest provides better generalization compared to a decision tree and efficiently manages small datasets by eliminating overfitting [13; 14; 22].  
A series of reasons are presented which suggest the use of DTs. DTs are chosen for their ability to interpret data, to handle non-linear relationships, and to be flexible with different data types. Referring to the ability to interpret, DTs are suitable for classification problems where comprehending the decision-making process is essential. The generated tree structure provides an easy tree visualization of the decision-making rules, making its structure clear, which is valuable in fields where transparency is crucial. DTs can handle non-linear relationships between features without the need for complex transformations, allowing them to be agile for datasets where the relationship between input variables and the target variable is not linear. Also, DTs can manage both categorical and numerical data, making them flexible for various types of datasets. In this research, the data provided is mainly numerical, but the future expansion of dataset type and size will also require it to include categorical or multivariate features. Finally, DTs are outlier resistant, as splits are made based on the majority of the data distribution [14; 22; 23].  
At the same time, NB is selected for its simplicity, classification speed, and efficiency when categorical data are used. Naive Bayes is a type of supervised learning, which means that the model is trained using labeled data. NB is a simple classification algorithm that is computationally effective, allowing the processing of large datasets or real-time prediction  
Figure 3: Process overview.  
cases. It operates well with high-dimensional data, where the number of attributes is relatively large compared to the number of data observations. Regarding the assumption of the attributes, independence can make the model simpler and more interpretable. On occasions where the features are actually independent, NB can perform exceptionally well. NB is also efficient for categorical data and is often used in text classification problems, which may occur in this paper's future research [14; 22; 23; 27].  
Finally, k-NN is suitable for its non-parametric nature and effectiveness with small datasets and complex decision boundaries that do not allow assumptions about the underlying data distribution. This makes it appropriate for datasets where the relationship between attributes and the target variable is complex and not easily captured by parametric models. K-NN is also easy to comprehend and build. It classifies data based on the most usual class among its nearest neighbors, making it a valid option for problems where simplicity is the main objective. K-NN works remarkably well with small datasets where the computational cost of discovering k-neighbors is affordable. It works well when the decision range is not well-defined by a simple linear or non-linear model. Based on the current statistical tests, non-parametric methods are used for the available dataset [14; 22; 23; 28; 29].