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Min-Max Scaling: This scales the data to a fixed range, usually 0 to 1. It’s beneficial when the standardization assumptions about data distribution don’t hold. Here’s how it’s done: from sklearn.preprocessing import MinMaxScaler min_max_scaler = MinMaxScaler() scaled_features =