Notice: This page requires JavaScript to function properly.
Please enable JavaScript in your browser settings or update your browser.
Lära Data Normalization | Normalization & Standardization
Preprocessing Data

Svep för att visa menyn

book
Data Normalization

Data Normalization & Standardization provides rescaling numerical data into the appropriate interval. For example, ML models usually process the values in the interval [0; 1]. It's much more convenient to process the finite data, and also the data that all scaled to the same interval. There are two approaches:

  • to normalize data: move it to the interval [0; 1]

  • to standartizate data.

Data normalization

After normalization, each value will be represented as the value from the interval [0;1]. This allows us to easily understand how close the value is to the left or right bound. There is a quick demo.

For example, you got the 4th value from column fare:

python

How can you know, how many it is? How close is it to the mean value? Is it many or not?

You have to know the bounds for the fare price. If you normalized the data, you would get the value:

python

which is more informative: the value is small enough, only 10% out of the max price.

Normalization formula is:

Let's implement it manually:

1234
x_min, x_max = data['Fare'].min(), data['Fare'].max() normalized_fare = (data['Fare'] - x_min) / (x_max-x_min) print(normalized_fare[3]) # output: 0.10364429745562033
copy

Great! Now we see that 4th value is 0.10364, so this price is only 10% out of the maximum one.

Of course, there is a built-in way that does all the work: let's use the MinMaxScaler() from sklearn.

1234567
import pandas as pd from sklearn.preprocessing import MinMaxScaler # reading data # creating a scaler scaler = MinMaxScaler() data_scaled = scaler.fit_transform(data) # now the data is normalized
copy
Uppgift

Swipe to start coding

Use the fit_transform() method with Fare data as an argument. Compare if the normalized fare value of 4th record is equal to the received manually one.

Note that your data should be a 2D-array.

Lösning

Switch to desktopByt till skrivbordet för praktisk övningFortsätt där du är med ett av alternativen nedan
Var allt tydligt?

Hur kan vi förbättra det?

Tack för dina kommentarer!

Avsnitt 4. Kapitel 1

Fråga AI

expand
ChatGPT

Fråga vad du vill eller prova någon av de föreslagna frågorna för att starta vårt samtal

book
Data Normalization

Data Normalization & Standardization provides rescaling numerical data into the appropriate interval. For example, ML models usually process the values in the interval [0; 1]. It's much more convenient to process the finite data, and also the data that all scaled to the same interval. There are two approaches:

  • to normalize data: move it to the interval [0; 1]

  • to standartizate data.

Data normalization

After normalization, each value will be represented as the value from the interval [0;1]. This allows us to easily understand how close the value is to the left or right bound. There is a quick demo.

For example, you got the 4th value from column fare:

python

How can you know, how many it is? How close is it to the mean value? Is it many or not?

You have to know the bounds for the fare price. If you normalized the data, you would get the value:

python

which is more informative: the value is small enough, only 10% out of the max price.

Normalization formula is:

Let's implement it manually:

1234
x_min, x_max = data['Fare'].min(), data['Fare'].max() normalized_fare = (data['Fare'] - x_min) / (x_max-x_min) print(normalized_fare[3]) # output: 0.10364429745562033
copy

Great! Now we see that 4th value is 0.10364, so this price is only 10% out of the maximum one.

Of course, there is a built-in way that does all the work: let's use the MinMaxScaler() from sklearn.

1234567
import pandas as pd from sklearn.preprocessing import MinMaxScaler # reading data # creating a scaler scaler = MinMaxScaler() data_scaled = scaler.fit_transform(data) # now the data is normalized
copy
Uppgift

Swipe to start coding

Use the fit_transform() method with Fare data as an argument. Compare if the normalized fare value of 4th record is equal to the received manually one.

Note that your data should be a 2D-array.

Lösning

Switch to desktopByt till skrivbordet för praktisk övningFortsätt där du är med ett av alternativen nedan
Var allt tydligt?

Hur kan vi förbättra det?

Tack för dina kommentarer!

Avsnitt 4. Kapitel 1
Switch to desktopByt till skrivbordet för praktisk övningFortsätt där du är med ett av alternativen nedan
Vi beklagar att något gick fel. Vad hände?
some-alt