Predict by removing unnecessary variables

Some variables haven t sufficient effectiveness to give an important result. We can say that these values perturbate the model.

I have choosed two methods to remove some unnecessary variables . 

-   Low correlations  (abs(corrélation)<0,1) : list 1 :householdsize, racePctAsian, agePct12t21, agePct16t24, agePct65up, pctUrban, pctWRetire, indianPerCap, PctEmplManu, PctEmplProfServ, PctWorkMomYoungKids, PersPerOccupHous, PctVacMore6Mos, MedOwnCostPctInc, MedOwnCostPctIncNoMtg, PctBornSameState, PctSameCity85, PctSameState85

-   Opposite variables or our targetted values on the two by two matrix on the kohonen clustering research  : list 2 : racePctWhite, pctUrban, pctWWage, pctWInvInc, pctWRetire, PctEmploy, PctFam2Par, PctKids2Par, PctYoungKids2Par, PctTeen2Par, PctWorkMom, PctSpeakEnglOnly, PersPerOccupHous, PersPerOwnOccHous, PctHousOccup, PctHousOwnOcc,MedYrHousBuilt, PctBornSameState, PctSameHouse85, PctSameCity85, PctSameState85

Removed values in two lists

	List	RMSE	MAE	MSE	ARV
linear regression	1	0,13825	0,10049	0,019112	0,52324
	2	0,134	0,097173	0,017957	0,49161
PLS regression	1	0,13671	0,098719	0,018691	0,5117
	2	0,13291	0,09554	0,017665	0,48362
SVM Polynomial	1	0,13048	0,091957	0,017025	0,46611
	2	0,12925	0,089951	0,016705	0,45733
Neural network	1	0,13788	0,09764	0,019011	0,52048
	2	0,13351	0,095503	0,017824	0,48797