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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 …