## 3 Implementation and Results  
Version 3.6.9 of the Python programming language was used to implement the models. The DNN model was applied with the aid of the Keras model. DL is the foundation for the deep multilayer perceptron architecture with regularization and dropout used in the developed DNN learning model. By making an analytical inquiry on the selected dataset and using ML/DL models with LR, NB, RF, SVM, KNN, and DNN algorithms supporting stratified K-fold (K = 5 and 10) cross-validation, the outcomes of the examination effort have been reported in this section. The results of the ML and DL models were presented for model evaluation and comparison after Hyperparameter tuning with stratified K-fold cross-validation was performed. The classification accuracy of the LR model using stratified five-fold cross-validation is shown in Figure 2. It demonstrates fold-3's superior accuracy. This model validatedthe stratified 5-fold cross-validation with a mean accuracy of 81.47% and a standard deviation (SD) of 0.0398. After Hyperparameter tuning, the best parameter was chosen at max_iter = 500 and solver = Newton-Cg using 5-fold stratified cross-validation. The stratified ten-fold cross-validation results of the LR model's classification accuracy are shown in Figure 3. It demonstrates that fold-1 has the best accuracy. The 10-fold stratified cross-validation was determined to be validated by the LR model's 82.64% mean accuracy at the time and SD of 0.0625. After Hyperparameter tuning, the best parameter was chosen with max_iter = 1000 and solver = Newton-Cg by stratified 10-fold cross-validation. The classification accuracy of the NB model using stratified five-fold cross-validation is shown in Figure 4. It demonstrates fold-3's superior accuracy. This model validated the stratified 5-fold cross-validation with a mean accuracy of 81.47% and a standard deviation (SD) of 0.0398. After Hyperparameter tuning, the best parameter of the Gaussian NB classifier was chosen at prior = none and var_smoothing = 1 \(\times\) 10\({}^{-6}\) using 5-fold stratified cross-validation. Figure 3: The classification accuracies of the LR model using the stratified ten-fold cross-validation. Figure 2: The classification accuracies of the LR model using the stratified five-fold cross-validation. The stratified ten-fold cross-validation results of the NB model's classification accuracy are shown in Figure 5. It demonstrates that fold-1 has the best accuracy. The 10-fold stratified cross-validation was determined to be validated by the NB model's 82.64% mean accuracy at the time and SD of 0.0625. After Hyperparameter tuning, the best parameter of the Gaussian NB classifier was chosen at prior = none and var_smoothing = \(1\times 10^{-6}\) using 5-fold stratified cross-validation. The classification accuracy of the RF model using stratified five-fold cross-validation is shown in Figure 6. It demonstrates fold-3's superior accuracy. This model validated the stratified 5-fold cross-validation with a mean accuracy of 85.22% and a standard deviation (SD) of 0.0485. After Hyperparameter tuning, the best parameter was chosen at n_estimators = 400 and criterion = Gini using 5-fold stratified cross-validation. Figure 4: The classification accuracies of the NB model using the stratified five-fold cross-validation-1. Figure 5: The classification accuracies of the NB model using the stratified ten-fold cross-validation-2. Figure 6: The classification accuracies of the RF model using the stratified five-fold cross-validation. The stratified ten-fold cross-validation results of the RF model's classification accuracy are shown in Figure 3.0 It demonstrates that fold-5 has the best accuracy. The 10-fold stratified cross-validation was determined to be validated by the RF model's 83.89% mean accuracy at the time and SD of 0.0982. After Hyperparameter tuning, the best parameter was chosen with n_estimators = 150 and criterion = Gini by stratified 10-fold cross-validation.