I have been working on a machine learning project and I have been trying to scale the features before feeding it to my model. I know Min-Max Normalization and Quantile Transformation scales down the features in the range of 0 and 1. I was wondering if there’s any other scaling method or way through which the range can be scaled down between 0 and 1.
Is there any data scaling methods except for Min-Max Normalization and Quantile transformation that keeps the range between [0,1]?
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RobustScaler(), to remove the outliers and then use eitherStandardScalerorMinMaxScalerfor preprocessing the data