## 4 Results  
The first experiment in this research used the ML learning algorithm without any hyper-tuning for the hyperparameter. This was applied twice, using the 20:80 and 30:70 testing:training ratio. Table 3 shows the obtained results [3]. The DT hyperparameters were hyper-tuned, including the criterion, max_depth, min_samples_leaf, and min_samples_split. Table 4 shows the values used for each parameter and the combination of the hyperparameters that was selected as the best estimator. The decision tree-trained model achieved an accuracy of 79% using five-fold cross-validation. The confusion matrix, true positive rate, and false positive rate were calculated for the SVM model, as shown on Figure 4a and Table 5. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**ML Model** & **Related Work Results * & **30:70 Testing:Training Ratio** & **20:80 Testing:Training Ratio** \\ \hline Decision Tree Classifier & 55.38\% & 71.00\% & 74.00\% \\ Support Vector Machine & 52.31\% & 74.00\% & 79.00\% \\ Random Forest Classifier & 75.38\% & 77.00\% & 86.00\% \\ \hline \hline \end{tabular}  
* Related work from [3], where a 30:70 testing:training ratio was used with no hyper-tuning.  
\end{table} 
Table 3: Comparison of accuracy results between the related work [3] and this research without applying hyper-tuning. The hyper-tuning was applied to the SVM for the C and Gamma hyperparameters. Table 6 shows the hyperparameter values and the combination that was selected as the best estimator parameters that trained the model to give the best accuracy of 84.00% using five-fold cross-validation. Figure 4b and Table 5 shows the confusion matrix for the SVM model with TPR and FPR values. The same hyperparameters applied to the RF, including the criterion, max_depth, min_samples_leaf, min_samples_split, and n_estimators. The parameter combination shown in Table 7 was selected as the best estimator and was used in training the algorithm. The algorithm with these parameters was able to outperform both the DT and SVM, with an accuracy of 95% using five-fold cross-validation. Figure 4c shows the confusion matrix for the RF with TPR and FPR. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Hyperparameter Name** & **Hyperparameter Values** & **Hyperparameter Optimal Value** \\ \hline C & 0.001, 0.01, 0.1, 1, 10 & 10 \\ Gamma & 0.001, 0.01, 0.1, 1 & 0.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Support vector machine best hyperparameter combination after hyper-tuning. Figure 4: Confusion matrix for ML algorithms. (**a**) DT confusion matrix; (**b**) SVM confusion matrix; (**c**) RF confusion matrix. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Hyperparameter Name** & **Hyperparameter Values** & **Hyperparameter Optimal Value** \\ \hline criterion & gini, entropy & entropy \\ max\_depth & 100, 200 & 100 \\ min\_samples\_leaf & 2, 3 & 2 \\ min\_samples\_split & 1, 2, 3, 4 & 3 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Decision Tree best hyperparameter combination after hyper-tuning. In our experiment, we found that the accuracy and TPR of the RF were the highest, whereas the FPR was the lowest among the other alternatives, as shown in Figure 5. An essential method for measuring the efficacy of an ML model is the receiver operating characteristic curve (ROC). This is a 2D ROC curve representing the relation between FPR and TPR and showing the prediction ability of the generated model. To examine the importance of input variables to the model, the variable importance plot, as shown in Figure 6, was used; it provides the most significant features in descending order based on a mean decrease in Gini. This result implies that the top variable has greater effect on the model results than the bottom one and has classification power. The plot shows that degree percentage, MBA percentage (mba_p), entry test result (etest_p), salary and student percentage, and field in secondary school (hsc_p, ssc_p) play important roles in predicting the appropriate major for students.