## 4 Experimentation and Results
### Support Vector Machine Results  
Extensive research has been performed on SVM, a machine learning model well respected for handling both regression and classification tasks [53]. In this work, we compared the performance of SVM with the TinyML method using data classification. Rooted in statistical learning theory, SVM is preferred for its ability to avoid local optima and distinguishes itself from other classification algorithms. SVM operates by determining ideal hyperplanes to define different data classes. It achieves this with kernel functions, therefore allowing SVM to operate efficiently even in cases where linear separation of data are not possible. Specifically, the Radial Basis Function (RBF) kernel is adept at managing nonlinear scenarios. The dimensions of the SVM plot are determined by the number of features in our dataset, where each data record is depicted as a point in a multi-dimensional space. SVM's objective is to identify a hyperplane, or a set of hyperplanes, that effectively separate the classes within this space. Importantly, SVM strives to maximize margins, which denote the distance between the classifier and the nearest training data points. Table 1 presents the results of the SVM learning model, showing how effective it was using two different evaluation methods; that is, train/test split and 10-fold cross-validation. In the train/test split section, the SVM model achieved an accuracy of 96.1%, indicating the percentage of correctly classified instances. Further information about the model's performance is provided by the precision and recall measures, which are 94.2% and 94.7%, respectively. While precision is the proportion of true positive predictions among all positive predictions, recall, on the other hand, is the proportion of real positives that are detected by the model properly. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model Validation Technique** & **Performance Metrics** & **SVM** & **TinyML** \\ \hline \multirow{4}{*}{Train/Test Split} & Accuracy & 96.10\% & 93.40\% \\  & Precision & 94.20\% & 94.10\% \\  & Recall & 95.70\% & 95.40\% \\ \hline \multirow{4}{*}{10 Fold Cross-Validation} & Accuracy & 97.20\% & 96.60\% \\  & Precision & 95.30\% & 95.20\% \\ \cline{1-1}  & Recall & 96.90\% & 96.30\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: SVM and TinyML learning results using train/test split and 10 Fold cross-validation. When the SVM model was applied to a 10-fold cross-validation, its accuracy increased to 97.2%, showing strong generalization across various subsets of the dataset. The results of the SVM model are summarized in Table 1; the precision and recall are 95.3% and 96.9%, respectively. During 10-fold cross-validation, the SVM model achieved an accuracy of 97.2%, demonstrating robust performance across different subsets of the dataset. The detailed results are shown in Table 1, with precision at 95.3% and recall at 96.9%. We conducted an analysis using a Shallow Neural Network (SNN), where we progressively added layers to improve its capabilities. Our evaluation criteria encompassed accuracy, precision, and recall. The outcomes of the SNN assessment are presented in Table 1. The model attained an accuracy of 93.4%, indicating how accurately the SNN predicted and classified instances. The results of the SNN analysis are shown in Table 1. Our model achieved an accuracy of 93.4%, showing the overall correctness of the SNN's predictions, or the percentage of correctly classified instances among the total. The precision rate results in 94.1%, indicating the accuracy of positive predictions, or the percentage of true positive instances among all instances predicted as positive by the SNN. Likewise, the recall rate, also called sensitivity or true positive rate, is 95.4%, showing the SNN's ability to correctly identify all relevant instances. When employing a 10-fold cross-validation approach, the overall correctness of predictions yielded an accuracy rate of 96.6%. For positive predictions, the precision reached 95.2%, with a recall rate of 96.3%.