Table 6. Tuning hyperparameters of best results of algorithms tested.

Algorithm	Conditions on the dataset	Tuning parameters	% Accuracy
Results on validation data (the best result)
K-Nearest Neighbors	Any	Neighbors=1	88.57
	Scaling	Neighbors: 1	71.43
	PCA	Neighbors: 1	82.86
	Scaling + PCA	Neighbors: 4	48.57
Support Vector Classifier	Any	C=10	8.57
	Scaling	C=70	94.29
	PCA	C=10	8.57
	Scaling + PCA	C=40	91.43
Logistic regression	Any	C=0,1	100.00
	Scaling	C=0,1	97.14
	PCA	C=0,1	94.29
	Scaling + PCA	C=0,1	94.29
Linear discriminant analysis	Any	Default	91.43
	Scaling	Default	91.43
	PCA	Default	97.14
	Scaling + PCA	Default	82.86
Gaussian NB	Any	Default	85.71
	Scaling	Default	85.71
	PCA	Default	80.00
	Scaling + PCA	Default	71.43
Random forest	Any	n_estimators=81, max_depth=91, min_samples_split=10, max_features=50	97.14
	Scaling	n_estimators=91, max_depth=81, min_samples_split=10, max_features=60	97.14
	PCA	n_estimators=91, max_depth=21, min_samples_split=10, max_features=30	94.28
	Scaling + PCA	n_estimators=61, max_depth=11, min_samples_split=10, max_features=20	85.71
Decision tree	Any	max_depth=71, min_samples_split=10, max_features=40	68.57
	Scaling	max_depth=51, min_samples_split=10, max_features=60	68.57
	PCA	max_depth=81, min_samples_split=10, max_features=30	82.85
	Scaling + PCA	max_depth=51, min_samples_split=20, max_features=60	74.28
Multi-layer perceptron	Any	Neurons=800	85.71
	Scaling	Neurons=50	91.43
	PCA	Neurons=300	97.14
	Scaling + PCA	Neurons=50	91.43
K-means	Any	Clusters=16	76.97
	Scaling	Clusters=14	68.34
	PCA	Clusters=16	73.38
	Scaling + PCA	Clusters=11	58.99