= correlated_features.index# the collection of feature variable names we'll drop due to their being correlated to other_1 in correlated_feature_variable_names:
for name_2 in correlated_feat
alpha, corr)) print(colX +' and ' +colY+ ' two ariables are not correlated') print(colX +' and ' +colY+ ' two variables are highly correlated '))Out [1]:
Pears
OVER (PARTITION BY NULL ORDER BY I.SAL DESC) WHERE I.EMPNO=O.MGR --correlatedOVER (PARTITION BY NULL ORDER BY I.SAL DESC) WHERE I.EMPNO=O.MGR --correlatedOVER (PARTITION BY NULL ORDER BY I.SAL DESC)
FROM SC