Table 7 Performance of the models – With Voting.

Voting algorithms	Class	Count	TP	TN	FN	FP		Precision	Recall	F1
GBT DT RF KNN	HR	272	245	713	29	27		0.894160584	0.900735294	0.897435897
	LR	406	353	507	101	53		0.777533	0.869458128	0.820930233
	MR	336	225	617	61	111		0.786713	0.669642857	0.723472669
	Total	1014	823	1837	191	191	Overall →	0.811637	0.811637	0.811637
							Weighted →	0.811859721	0.81163708	0.809158832
GBT RF	HR	272	251	711	31	21		0.890070922	0.922794118	0.906137184
	LR	406	364	514	94	42		0.794759825	0.896551724	0.842592593
	MR	336	231	646	43	105		0.843065693	0.6875	0.757377049
	Total	1014	846	1871	168	168	Overall →	0.834319527	0.834319527	0.834319527
							Weighted →	0.836333188	0.83431953	0.831400981
GBT RF DT	HR	272	246	714	28	26		0.897810219	0.904411765	0.901098901
	LR	406	356	516	92	50		0.794642857	0.876847291	0.833723653
	MR	336	235	621	57	101		0.804794521	0.699404762	0.748407643
	Total	1014	837	1851	177	177	Overall →	0.825443787	0.825443787	0.825443787
							Weighted →	0.825680807	0.82544379	0.823526304

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