## 6 Experimental Results and Discussion  
In this section, the findings of our experiments are given. The online machine learning repository is used to gather the data, which is then polished and standardized. Following standardization, hyperparameter and machine classifiers are used. All of the classifiers are made up of 75% training data and 25% testing data. Before and after standardized datasets, the accuracy of classifiers is also examined. The accuracy of the selected classifiers is shown for assessment purposes. Figure 11 depicts the accuracy of classifiers before and after the standardization of the data. Actually, the majorities of listed algorithms are not based on neural networks (LR, KNN, SVM, Nu SVC, DT, RFC, AdaBoost, GBC, NB, LDA, Q DA, NN, k-fold cross validation and ensemble technique) and increased their accuracy, as shown in Figure 8. On the standardized dataset, the accuracy of Naive Bayes and Support Vector Machine classifiers fell. On the standardized dataset, certain classifiers, including RF, DT, GB, and NN, exhibited considerable accuracy gains. The RF and DTC reach the maximum prediction accuracy of 100% and 98.80%, respectively, as shown in Figure 10. The Support Vector Machine has the lowest overall presentation and the lowermost accuracy of 68.80%. We also look at the dataset's correctness before and after normalization. The Random Forest and Decision Tree classifiers reach an accuracy of 100% and 98.80%, respectively, demonstrating the influence of the dataset normalized. Figures 12 and 13 and Table 2 are for positive classes. The random forest classifier achieves 100% recall, precision, F-measure, and accuracy, whereas, for negative classes, the decision tree classifier achieves 100% recall, precision, F-measure, and accuracy. However,  
Figure 11: Categorical features correlation (Cramer’sV). random forest has the best memory, precision, F-measure, and accuracy of 100%. All classifiers have a precision of 100%, whereas all classifiers have a recall better than any other which is of 100%. Random forest and decision tree both have a maximum F1-score of 100%. SVM has a low recall, precision, and F1-score of 69% and it has the lowest recall, F-measure, and accuracy of 69%, and random forest and the gradient boosting classifier achieve a maximum ROC-AUC of 100%. As a result, the negative class had a ROC-AUC of 73%, which was much lower than the positive class. Random forest has a fairly good performance, with 100% recall, precision, F-measure, and ROC-AUC, as well as 100% accuracy. Figure 12: Classifiers Accuracy. Figure 13: Results of different machine learning models. As shown in Figures 14 and 15 and Table 3, the decision tree classifier achieved the highest accuracy of 99.60 and precision of 100%, the random forest classifier achieved 100% recall, and the gradient boosting classifier achieved 100% ROC-AUC. NB has a low accuracy of 82.15%, precision of 86.60%, F1-score of 83.12%, and ROC-AUC of 90.67%. The confusion matrix of an experiment's expected outcomes is depicted in Figure 16. We also have analyses of the model's performance using ROC-AUC. The receiver operating characteristic curve (ROC) is a curve with a true positive rate on the ordinate and a false positive rate on the abscissa [40]. It is created from a series of varied boundary values. The AUC, or area under the ROC curve, indicates the likelihood that the positive sample's calculated score will be greater than the negative sample's computed value. When the samples are chosen at random, the advantages and disadvantages of the prediction model may be investigated. As illustrated in Figure 17, which also illustrates an experiment's ROC curve, our model's average AUC value is 100%.