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![Min-max scaling
● Squeezes (or stretches) all values within the range of [0, 1] to add robustness to
very small standard d...](https://image.slidesharecdn.com/qconsp17-featureengineering-170426171227/75/Feature-Engineering-Getting-most-out-of-data-for-predictive-models-30-2048.jpg?cb=1666142952)
![>>> from sklearn import preprocessing
>>> X = [[ 1., -1., 2.],
... [ 2., 0., 0.],
... [ 0., 1., -1.]]
>>> X_normalized = p...](https://image.slidesharecdn.com/qconsp17-featureengineering-170426171227/75/Feature-Engineering-Getting-most-out-of-data-for-predictive-models-33-2048.jpg?cb=1666142952)
