Course Content

Introduction to Scikit Learn

1. The Very First Steps

Loading Data Sets SimpleImputer Ordinal Encoding

2. Scaling Numerical Data

MinMaxScaler MaxAbsScaler StandardScaler

3. Models in Scikit Learn

Prepearing Data Set 1/2 Prepearing Data Set 2/2

StandardScaler

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:

[object Object]

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.


              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.

Task

You have wine dataset, we have worked with it recently. Please, standardize this data. To check, if StandardScaler function works correct, please dispay the mean and standard deviation. Pay attention: mean will be equal to 0 and std to 1.

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Everything was clear?

Thanks for your feedback!

Section 2. Chapter 3

StandardScaler

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:

[object Object]

Here we have the following values:

x_scaled - standardized feature element,
x - unnormalized feature element,
mean - mean value,
std - standard deviation value.

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


              1
            
from sklearn.preprocessing import StandarsScaler

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

Task

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Everything was clear?

Thanks for your feedback!

Section 2. Chapter 3

StandardScaler

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:

[object Object]

Here we have the following values:

x_scaled - standardized feature element,
x - unnormalized feature element,
mean - mean value,
std - standard deviation value.

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


              1
            
from sklearn.preprocessing import StandarsScaler

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

Task

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Everything was clear?

Thanks for your feedback!

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:

[object Object]

Here we have the following values:

x_scaled - standardized feature element,
x - unnormalized feature element,
mean - mean value,
std - standard deviation value.

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


              1
            
from sklearn.preprocessing import StandarsScaler

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

Task

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Section 2. Chapter 3

Switch to desktop for real-world practiceContinue from where you are using one of the options below