Artificial Intelligence Based Customer Churn Prediction Model for Business Markets

Table 2

Result Analysis of Various Feature Selection methods on Applied Dataset

No. of Iterations	Dataset-1
	CSSO-FS	SSO-FS	KHO-FS	GWO-FS
100	1.502	2.911	3.756	4.235
200	1.502	2.911	3.749	4.235
300	1.502	2.911	3.749	3.807
400	1.502	2.911	3.749	3.807
500	1.498	2.911	3.738	3.807
600	1.498	2.911	3.738	3.807
700	1.498	2.911	4.252	3.807
800	1.498	2.911	4.252	3.807
900	1.498	2.911	4.252	3.807
1000	1.498	2.911	3.721	3.807
Average	1.500	2.911	3.896	3.892
No. of Iterations	Dataset-2
	CSSO-FS	SSO-FS	KHO-FS	GWO-FS
100	1.620	3.025	3.858	3.936
200	1.620	3.025	3.858	3.932
300	1.620	3.025	3.838	3.932
400	1.620	3.025	3.838	3.932
500	1.614	3.015	3.838	3.932
600	1.614	3.015	3.838	3.932
700	1.614	3.015	3.838	3.932
800	1.593	3.005	3.838	3.932
900	1.593	3.005	3.838	3.930
1000	1.591	3.005	3.838	3.930
Average	1.610	3.016	3.842	3.932
No. of Iterations	Dataset-3
	CSSO-FS	SSO-FS	KHO-FS	GWO-FS
100	1.5325	2.9490	3.7770	3.8609
200	1.5325	2.9480	3.7770	3.8581
300	1.5325	2.9480	3.7770	3.8581
400	1.5325	2.9480	3.7770	3.8575
500	1.5216	2.9480	3.7750	3.8560
600	1.5216	2.9480	3.7730	3.8560
700	1.5194	2.9480	3.7730	3.8555
800	1.5194	2.9480	3.7730	3.8555
900	1.5191	2.9480	3.7730	3.8555
1000	1.5186	2.9480	3.7730	3.8555
Average	1.5250	2.9481	3.7748	3.8569