Among the classifiers, J48, REP Tree, MLP, and Random Forest showed accuracy above 80%, with Random Forest achieving the highest accuracy of 85% (Figures ). To evaluate the classifiers, we used cross validation, which involves dividing the dataset into ten folds and testing each fold on the remaining nine folds for training, repeating the process ten times for each distinct fold. The results of each test were then averaged to obtain the accuracy of each classifier. Cross validation helps in reducing the variance of the estimate, making the results more reliable. Figure shows the ROC (receiver operating characteristic) curve for the ML classifier. This curve reflects the performance of the model at various classification thresholds. The X-axis of the curve plots the false positive rate, and the Y-axis-the true positive rate. The area under the curve (AUC) is 0.886 which is close to 1. AUC provides an aggregate measure of performance across all possible classification thresholds, the higher is the AUC, the better is the performance. The comparison of these rates through the curve allows for the identification of how likely the model is to maximize true positives and minimize false positives in the data. Figure represents the precision-recall curve for the ML model, which is the measure between precision and recalls for different thresholds. As the curve approaches the top right of the graph, it demonstrates that the model has good prediction accuracy.