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transforms the data to have a mean of zero and a standard deviation of one. It’s useful when the data follows a Gaussian distribution. In Python, this can be implemented as follows: from sklearn.preprocessing import StandardScaler scaler = StandardScaler() scaled_features = scaler.fit_transform(df)