How does Z-score normalization work?
An example of an AI chatbot conversation about Z-score normalization in data processing.
Can you explain how Z-score normalization works?
Sure! Z-score normalization, also known as standardization, transforms data to have a mean of 0 and a standard deviation of 1.
Why is it important to normalize data?
Normalizing data helps to ensure that different features contribute equally to the analysis, improving model performance.
How do you calculate the Z-score?
The Z-score is calculated using the formula: Z = (X - μ) / σ, where X is the value, μ is the mean, and σ is the standard deviation.
Can you give me an example of Z-score normalization?
Absolutely! If you have a dataset with values [10, 20, 30], the mean is 20 and the standard deviation is about 10. The Z-scores would be [-1, 0, 1].
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