Z-score normalization is a technique for transforming data so that it has a mean of 0 and a standard deviation of 1. This is done by subtracting the mean from each value and then dividing by the standard deviation.

Z-score normalization is often used as a preprocessing step for machine learning algorithms. It can help to improve the performance of these algorithms by making the data more evenly distributed and by reducing the impact of outliers.

The formula for z-score normalization:

z = (x - μ) / σ

where:

x is the original value

μ is the mean of the data

σ is the standard deviation of the data

Z-score normalization can be applied to any type of data, but it is most commonly used with continuous data. It is not typically used with categorical data, such as labels or names.

Example of how z-score normalization can be used to transform a dataset:

Let's say we have a dataset of heights in centimeters. The mean height is 170 cm and the standard deviation is 10 cm. 

To normalize the heights, we would subtract the mean (170 cm) from each height and then divide by the standard deviation (10 cm). 

This would give us a new dataset of z-scores, where the mean is 0 and the standard deviation is 1.

Z-score normalization is a simple but effective technique that can be used to improve the performance of machine learning algorithms. It is a good choice for preprocessing data that is continuous and has a normal distribution.

Advantages of using z-score normalization:

• It makes the data more evenly distributed, which can help to improve the performance of machine learning algorithms.
• It reduces the impact of outliers, which can also improve the performance of machine learning algorithms.
• It is a simple and straightforward technique to implement.

Disadvantages of using z-score normalization:

• It can be sensitive to outliers, if the outliers are extreme.
• It can make the data less interpretable, as the original units of measurement are lost.