Data Preprocessing for Machine Learning: Standardization and Normalization
Prepare data for modeling by scaling features to comparable ranges using standardization and normalization techniques.
Raw data often contains features with different scales and units, causing algorithms to weight larger-magnitude features disproportionately. Standardization (Z-score normalization) transforms features to zero mean and unit variance by subtracting the mean and dividing by standard deviation. Normalization (min-max scaling) rescales values to a fixed range, typically [0,1], by subtracting the minimum and dividing by range. Distance-based algorithms (KNN, SVM, K-Means) require scaling; tree-based models don't. Standardization preserves outlier information but creates values outside typical ranges; normalization bounds values but is sensitive to extreme values. Robust scaling uses median and interquartile range for outlier-resistant transformation. Feature scaling must use training…
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