I'm working off my first independent project for some pattern classification. I'm utilizing some datasets from UCI machine learning, but am not sure on how to start with data normalization. The data isn't that large (feature vector around 15-20 dimensions), but I'm thinking there still needs to be some type of normalization done in order for my classifiers down the line (SVM + KNN) performs properly
I guess my main question is regarding when to normalize. I currently don't have my data split between testing and training data. Thus, should I normalize all data now?
Thanks ahead of time