What is Z-Score Normalization?
An example of an AI chatbot conversation about Z-Score Normalization in data processing.
Can you explain what Z-Score Normalization is?
Sure! Z-Score Normalization, also known as standardization, is a technique used to scale data. It transforms the data to have a mean of 0 and a standard deviation of 1.
Why is Z-Score Normalization important in data analysis?
It's important because it allows different features to be compared on the same scale, which can improve the performance of machine learning algorithms.
How do you calculate the Z-Score for a data point?
The Z-Score is calculated using the formula: Z = (X - μ) / σ, where X is the data point, 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 a mean of 50 and a standard deviation of 10, a data point of 60 would have a Z-Score of 1.0.
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