Normalization is the transformation of features so that they are on a similar scale, usually between 0 and 1 or -1 and 1. This can help reduce the influence of outliers and improve the performance of some machine learning algorithms that are sensitive to the scale of the features, such as gradient descent, k-means, or k-nearest neighbors. References: [Feature scaling - Wikipedia], [Normalization vs Standardization — Quantitative analysis]
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