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Lære Challenge: Scaling the Features | Preprocessing Data with Scikit-learn
ML Introduction with scikit-learn
course content

Kursinnhold

ML Introduction with scikit-learn

ML Introduction with scikit-learn

1. Machine Learning Concepts
2. Preprocessing Data with Scikit-learn
3. Pipelines
4. Modeling

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Challenge: Scaling the Features

In this challenge, you need to scale the features using StandardScaler. The data is the penguins dataset (encoded and with no missing values).

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import pandas as pd df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/a65bbc96-309e-4df9-a790-a1eb8c815a1c/penguins_imputed_encoded.csv') print(df)
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Here is a little reminder of the StandardScaler class.

Oppgave

Swipe to start coding

  1. Import the class that standardizes features by making the mean equal to 0 and the variance equal to 1.
  2. Initialize the scaler.
  3. Scale the X matrix of features.

Løsning

Switch to desktopBytt til skrivebordet for virkelighetspraksisFortsett der du er med et av alternativene nedenfor
Alt var klart?

Hvordan kan vi forbedre det?

Takk for tilbakemeldingene dine!

Seksjon 2. Kapittel 11
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book
Challenge: Scaling the Features

In this challenge, you need to scale the features using StandardScaler. The data is the penguins dataset (encoded and with no missing values).

12345
import pandas as pd df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/a65bbc96-309e-4df9-a790-a1eb8c815a1c/penguins_imputed_encoded.csv') print(df)
copy

Here is a little reminder of the StandardScaler class.

Oppgave

Swipe to start coding

  1. Import the class that standardizes features by making the mean equal to 0 and the variance equal to 1.
  2. Initialize the scaler.
  3. Scale the X matrix of features.

Løsning

Switch to desktopBytt til skrivebordet for virkelighetspraksisFortsett der du er med et av alternativene nedenfor
Alt var klart?

Hvordan kan vi forbedre det?

Takk for tilbakemeldingene dine!

Seksjon 2. Kapittel 11
Switch to desktopBytt til skrivebordet for virkelighetspraksisFortsett der du er med et av alternativene nedenfor
Vi beklager at noe gikk galt. Hva skjedde?
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