Table 4 Results obtained from the machine learning models after applying min-max scaling.

Model	Accuracy	Precision	Recall	F1 Score	Cohen’s Kappa	Log Loss
Logistic Regression	0.8480	0.8479	0.8480	0.8479	0.6898	0.3780
Naive Bayes	0.8824	0.8822	0.8824	0.8822	0.7595	0.5216
Support Vector Machine	0.8676	0.8701	0.8676	0.8662	0.7261	N/A
Decision Tree	0.8137	0.8152	0.8137	0.8142	0.6224	6.7140
Random Forest	0.8873	0.8871	0.8873	0.8870	0.7692	0.3026
Light Gradient Boosting Machine	0.8824	0.8822	0.8824	0.8822	0.7595	0.2801
CatBoost	0.8971	0.8973	0.8971	0.8966	0.7887	0.2734
K-Nearest Neighbors	0.8725	0.8745	0.8725	0.8713	0.7366	1.1207
Gradient Boosting Machine	0.9020	0.9021	0.9020	0.9017	0.7991	0.2773
AdaBoost	0.8431	0.8431	0.8431	0.8431	0.6803	0.6707
Linear Discriminant Analysis	0.8186	0.8206	0.8186	0.8191	0.6328	0.4072
Artificial Neural Network	0.8480	0.8479	0.8480	0.8479	0.6898	0.3860

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