Table 5 Results obtained from the machine learning models after applying normalization.

Model	Accuracy	Precision	Recall	F1 Score	Cohen’s Kappa	Log Loss
Logistic Regression	0.6225	0.6700	0.6225	0.6167	0.2735	0.6201
Naive Bayes	0.7745	0.7885	0.7745	0.7755	0.5514	1.0875
Support Vector Machine	0.6814	0.7091	0.6814	0.6812	0.3754	N/A
Decision Tree	0.8088	0.8091	0.8088	0.8089	0.6108	6.8906
Random Forest	0.8824	0.8823	0.8824	0.8820	0.7589	0.3278
Light Gradient Boosting Machine	0.8873	0.8874	0.8873	0.8873	0.7705	0.3462
CatBoost	0.8873	0.8871	0.8873	0.8870	0.7692	0.2872
K-Nearest Neighbors	0.6765	0.6811	0.6765	0.6777	0.3477	2.5868
Gradient Boosting Machine	0.8824	0.8822	0.8824	0.8822	0.7595	0.3129
AdaBoost	0.8725	0.8730	0.8725	0.8727	0.7409	0.6456
Linear Discriminant Analysis	0.8235	0.8290	0.8235	0.8243	0.6451	0.3976
Artificial Neural Network	0.6618	0.6983	0.6618	0.6598	0.3421	0.5971

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