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Feature scaling is a data pre-processing step where the range of variable values is standardized. Standardization of datasets is a common requirement for many machine learning algorithms. Popular feature scaling types include scaling the data to have zero mean and unit variance, and scaling the data between a given minimum and maximum value.

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what is difference between fit and fit_transform in sklearn while applying feature scaling [duplicate]

I have seen few post related to this question but i am not quite clear about my confusions as mention bellow. I have some confusion related to fit and fit_transform. suppose, I have X_train and X_test …
Chirag Palan's user avatar