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Oppiskele Challenge: Scaling the Features | Preprocessing Data with Scikit-learn
ML Introduction with scikit-learn

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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).

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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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.

Tehtävä

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.

Ratkaisu

import pandas as pd
from sklearn.preprocessing import StandardScaler

df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/a65bbc96-309e-4df9-a790-a1eb8c815a1c/penguins_imputed_encoded.csv')
# Assign X,y variables
X, y = df.drop('species', axis=1), df['species']
# Initialize a sclaer and scale the X matrix
scaler = StandardScaler()
X = scaler.fit_transform(X)
print(X)

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Osio 2. Luku 11
import pandas as pd
from sklearn.preprocessing import ___

df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/a65bbc96-309e-4df9-a790-a1eb8c815a1c/penguins_imputed_encoded.csv')
# Assign X,y variables
X, y = df.drop('species', axis=1), df['species']
# Initialize a sclaer and scale the X matrix
scaler = ___
X = ___
print(X)
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