Table 1.

Hyper-parameters settings and python library used for implementation.

Algorithm type	Classifiers	Train-test split	Hyper-parameters	Python library
Supervised machine learning algorithm	Logistics regression		• Solver = “lbfgs” • Penalty = “l2”	sklearn.linear_model
	Gaussian Naïve Bayes		• Variance smooting = 1e-09	sklearn.naive_bayes
	Decision tree		• Quality of split criterion = “gini” • Value of max_depth was varied between range (1-11 with increment of 1) • Maximum number of features to consider = “auto”	sklearn.tree
	Random forest		• Quality of split criterion = “gini” • Maximum depth of trees = 11 • Maximum number of features to consider = “auto” • Number of trees in the forest = 10	sklearn.ensemble
	AdaBoost		• Learning rate was varied between range (0.01-1.1 with increment of 0.01) • Maximum number of estimators at which boosting is terminated was varied between range (50-200 with increment of 10) • Algorithm = “SAMME.R”	sklearn.ensemble
	K-nearest neighbors		• Number of neighbors required was set to 2	sklearn.neighbors
	K-nearest neighbors	70-30% and 10-fold cross validation	• Number of neighbors required set at 5	sklearn.neighbors
Unsupervised machine learning algorithm	Affinity propagation		• Damping factor was set at 0.8 to maintain current value relative to incoming value (weight 1-damping) • Maximum iteration = 200 • Maximum number of iterations with no change in number of estimated clusters = 15	sklearn.cluster
	BIRCH		• Threshold from which the radius of subcluster should be lesser = 0.5 • Number of clusters = length of unique ids in training set (default = 2)	sklearn.cluster
	DBSCAN		• Maximum distance between two samples for consideration as neighbors (eps) = 0.50 • Minimum samples in neighborhood of a point to consider it as core point = 9 • Distance calculation method = “eulidean”	sklearn.cluster
	K-mean		• Number of neighbors required was set to 2	sklearn.cluster
	Mini-batch K-mean		• Number of neighbors required was set to 2	sklearn.cluster
	Mean shift		• Number of clusters = length of unique ids in training set (default = 2)	sklearn.cluster
	OPTICS		• Maximum distance between two samples for consideration as neighbors (eps) = 0.80 • Minimum samples in neighborhood of a point to consider it as core point = 10	sklearn.cluster