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I have data that have been grouped in 27 groups by different criteria. The reason for these groupings is to show that each group has a different behavior. However, I would like to normalize everything to the same scale. For example, I want to normalize to a 0-1 scale or 0-100, that way I could say something like 43rd percentile and it would have the same meaning across groups. If I were to just, say, standardize each individually by subtracting the mean of each and dividing by standard deviation work? Would I have to calculate the mean/st. dev of all of the combined data or do each of the 27 groups individually?

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You can normalize each criteria independently in values between 0 and 1 without taking into account the other criterias, it will work better for most classification methods k-nearest neighbors, random forest, neural network, etc.

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