if you're doing regressionusually that' not big deal butother models don' perform so well unless those values are scaled down first to common scale if you're not carefulyou can end up with some attributes counting more than others maybe the income would end up counting much more than the ageif you were trying to treat those two values as comparable values in your model so this can introduce also bias in the attributeswhich can also be problem maybe one set of your data is skewedyou knowsometimes you need to normalize things versus the actual range seen for that set of values and not just to to whatever the maximum is scale there' no set rule as to when you should and shouldn' do this sort of normalization all can say is always read the documentation for whatever technique you're using sofor examplein scikit-learn their pca implementation has xijufo option that will automatically normalize your data for you you should probably use that it also has some preprocessing modules available that will normalize and scale things for you automatically as well be aware too of textual data that should actually be represented numericallyor ordinally if you have zft or op data you might need to convert that to or and do that in consistent matter so againjust read the documentation most techniques do work fine with rawunnormalized databut before you start using new technique for the first timejust read the documentation and understand whether or not the inputs should be scaled or normalized or whitened first if soscikit-learn will probably make it very easy for you to do soyou just have to remember to do itdon' forget to rescale your results when you're done if you are scaling the input data if you want to be able to interpret the results you getsometimes you need to scale them back up to their original range after you're done if you are scaling things and maybe even biasing them towards certain amount before you input them into modelmake sure that you unscale them and unbias them before you actually present those results to somebody or else they won' make any senseand just little remindera little bit of parable if you willalways check to see if you should normalize or whiten your data before you pass it into given model no exercise associated with this sectionit' just something want you to remember ' just trying to drive the point home some algorithms require whiteningor normalizationsome don' soalways read the documentationif you do need to normalize the data going into an algorithm it will usually tell you soand it will make it very easy to do so please just be aware of that