If the dataset is standardized, it will have a good optimization effect for many machine learning algorithms. To get standardized data you have to use the next formula:

Here we have the following values:
- **x_scaled** - standardized feature element,
- **x** - unnormalized feature element,
- **mean** - mean value,
- **std** - standard deviation value.

There is a function in the **sklearn** library that normalizes data according to the formula given above: `MaxAbsScaler()`. In order to work with this function, it must first be imported in such a way:

The main property of standardized data is that this data: `mean = 0` and `standard deviation = 1`.

from sklearn.preprocessing import StandarsScaler

This function works like the previous two, namely `MinMaxScaler`, `MaxAbsScaler`, and it works in a similar way. So, in this chapter there is no example of using `StandartScaler` function. You will use it on your own in the below task.

Let's try! If you have some difficulties, please, use hints.

StandardScaler

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