Feature scaling is a technique for normalising a set of independent variables or data components. It is also known as data normalisation in data processing and is usually done during the data preprocessing step.
Normalization is useful when your data has variable scales and the technique you're employing, such as k-nearest neighbours and artificial neural networks, doesn't make assumptions about the distribution of your data. The assumption behind standardisation is that your data follows a Gaussian (bell curve) distribution.
Normalization is the process of rescaling values into a range of [0,1]. Typically, standardisation entails rescaling data to a mean of 0 and a standard deviation of 1. (unit variance).
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