do we always need to normalize our data ? or is it just for when we process Logistic Regression ?
i don't understand this part below
# split features from target variable
trainLabel = np.asarray(dfTrain['Outcome'])trainData = np.asarray(dfTrain.drop('Outcome',1))testLabel = np.asarray(dfTest['Outcome'])testData = np.asarray(dfTest.drop('Outcome',1))
why do we need to do this ?