## 4 Results  
The following section presents the empirical outcomes of our classification experiments, beginning with an analysis of the importance of features in elucidating model interpretability and then comparative performance metrics for random forest, SVM, and KNN classifiers. We report stratified five-fold cross-validation results--accuracy, macro-averaged precision, recall, F1-score, and ROC-AUC--alongside independent test set evaluations and execution time measurements to demonstrate predictive robustness and computational efficiency. To enhance interpretability, Figure 3 presents a bar chart of the top ten most influential features driving the model's recommendations. This visualisation demonstrates that terms such as \"cost estimation,\" \"risk assessment,\" and \"time series\", which correspond to quantitative methodologies like regression and time-series analysis, carry the most significant predictive weight. Concurrently, qualitative indicators, such as \"survey,\" \"interview,\" and \"case study\", rank prominently, indicating the model's capacity to capture both numerical and contextual cues. This analysis bolsters confidence in the system's decision logic by directly linking these feature importances to familiar methodological descriptors. The provided output illustrates machine learning models, namely RF, KNN, and SVM, that were trained and evaluated on a dataset. The RF model achieved an accuracy of 93.75%, indicating that approximately 93.75% of the instances were correctly classified (see Table 4). Precision was 92.11%, suggesting that when the RF model predicted a particular class, it was correct around 92.11% of the time. The recall score was 94.74%, indicating that the RF model correctly identified around 94.74% of the instances belonging to a particular class. An average squared difference of 73.03 between the actual and projected values. The value 6.84 was the average absolute difference between the actual and anticipated values. RF model performed with the highest accuracy on four different data portions (with a mean of 90%), indicating the model's generalisability for different data subsets (see Figure 4). The ROC-AUC value was 0.938, indicating the good discriminative ability of the model (see Figure 5). Figure 3: The most relevant feature scores. The KNN model achieved an accuracy of 84.38%. Precision was 86.81%, indicating that the KNN model correctly identified around 86.81% of the instances when predicting a particular class. The recall score was 83.33%, indicating that the KNN model correctly identified around 83.33% of the instances belonging to a specific class (see Table 4). KNN was the weakest method for accurate classifications in the constructed dataset, illustrating a non-adaptable approach (see Figure 6). The AUC value was 0.882, suggesting the model's good discriminative ability (see Figure 7). The SVM model also achieved an accuracy of 84.38%. Precision was 97.29%, indicating that the SVM model correctly identified around 97.29% of the instances when predicting a particular class. The recall score was 85.29%, suggesting that the SVM model correctly identified around 85.29% of the instances belonging to a specific class. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Model** & **Accuracy \(\pm\) SD** & **Precision** & **Recall** & **F1-Score** & **ROC-AUC** \\ \hline Random Forest & 93.8 \(\pm\) 1.9\% & 0.93 & 0.94 & 0.93 & 0.94 \\ \hline SVM & 84.4 \(\pm\) 3.1\% & 0.85 & 0.84 & 0.84 & 0.92 \\ \hline K-Nearest Neighbours & 84.4 \(\pm\) 3.5\% & 0.86 & 0.83 & 0.83 & 0.88 \\ \hline \end{tabular}
\end{table} 
Table 4: Cross-validated classification performance (macro-averaged) for each model. Figure 4: Accuracy for RF classifier using cross-validation. Figure 5: The ROC-AUC value for the RF model. The SVM model had the highest accuracy for the fifth data portion (around 83.5%), specifying the variable behaviour of the model for diverse subsets (see Figure 8). The AUC value was 0.924, indicating the good discriminative ability of the model (see Figure 9).