Z-score normalization is a technique in data preprocessing where the values in a feature are scaled based on the mean and standard deviation of the feature. This results in a distribution with a mean of zero and a standard deviation of one.
Utilizing Z-Score Normalization
Also known as standardization, this technique is essential for models that assume features are normally distributed, like in linear regression or k-means clustering. It ensures that each feature contributes proportionally to the final prediction.
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