## 4 Results and Discussion
As ML algorithms cannot directly work on categorical data, and to convert input features in digital form, we used a hot encoding technique. Hot encoding is a technique that can map categorical data into integers; as a result, the MI algorithm can produce better results. Hot encoding is useful when there is no relationship between the variables. The 10-fold cross-validation method was used to increase the generalization ability of the models and to ensure that the model behavior was optimal. Furthermore, accuracy, true positive rate (TPR), false-positive rate (FPR), and receiver operating characteristic (ROC) curve were used as evaluation metrics. The accuracy represents the percentage of correct predictions of the model given unseen data. In ML, the TPR is also known as recall or sensitivity and indicates the percentage of actual positive values that are correctly predicted by the model. Finally, the ROC curve plots the TPR of the model [89]. The current study performance metrics are shown below. TPR = TP/TP + FN  
FPR = FP/FP + TN  
Accuracy = TP + TN/TP + TN + FP + FN  
**Notes:** TP (true positive), FN (false negative), TN (true negative), FP (false positive). DT algorithms belong to the supervised category of ML algorithms. A DT is simple and easily understandable. We selected this technique because it is widely used by researchers due to its simplicity. In addition, a DT has some great advantages, such as representing rules that could be easily understood and interpreted by users [81]. This type of algorithm performs well for categorical and numerical attributes and does not require complex data preparation. In short, ML classifiers and their outputs are easy to understand for individuals with a non-analytical background [90]. The default parameters (ccp_alpha = 0.0, criterion = 'gini', min_samples_split = 2) are used to train the DT model, and an accuracy of 55% was obtained by DT using 10-fold cross-validation. Table 3 shows that the DT correctly classifies student specialization with a TPR of 0.87 and an FPR of 0.71. In the second step of the first experiment, RF classifiers were used to predict student specialization given a student placement dataset. The RF classifier is a supervised learning algorithm that applies to both classification and regression problems [80]. RF creates multiple DTs from random sample data and then gives predictions on high-accuracy trees [91, 92]. The RF classifier predicts the student field specialization with the following default parameters (bootstrap = True, ccp_alpha = 0.0, criterion = 'gini', max_depth = 15, \begin{table}
\begin{tabular}{c c c} \hline \hline  & **TN = 10 (0.29)** & **FP = 25 (0.71)** \\ \hline
**Actually (0)** & FN = 4 (0.13) & TP = 26 (0.87) \\ \hline
**Actually (1)** & **Predicted (0)** & **Predicted (1)** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Confusion matrix of the decision tree classifier. max_features = 'auto',max_leaf_nodes = 10). The TPR of RF is 0.70, and the FPR is 0.20, as shown in Table 4.  
Extra tree classifiers (ETC) are used in the third step to predict the student's field of specialization. ETC is an ensemble learning technique that collects the result of multiple trees. The approach is similar to an RF classifier, but the tree construction method differs. The accuracy of the ETC classifier on the student dataset is 0.52. During training, the ETC Classifier used the default parameters (n_estimators = 100, random_state = 0) to predict student specialization with high accuracy. The TPR and FPR of the ETC classifier were 0.53 and 0.49, respectively, as shown in Table 5.  
In the fourth step of the first experiment, we used the SVM classifier. SVM is a supervised learning algorithm that is mostly used for classification problems. SVM finds the hyperplane that divides a dataset into two classes [93]. SVM classifiers perform well on clean and small datasets. Furthermore, SVM is faster than other machine learning techniques [93].