Lära Challenge: Encoding Categorical Variables | Preprocessing Data with Scikit-learn

To summarize the previous three chapters, here is a table showing what encoder you should use:

In this challenge, you have the penguins dataset file (with no missing values). You need to deal with all the categorical values, including the target ('species' column).

Here is the reminder of the data you will work with:


              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.csv')

print(df.head())

Keep in mind that 'island' and 'sex' are categorical features and 'species' is a categorical target.

Uppgift

Swipe to start coding

Encode all the categorical values. For this, you need to choose the correct encoder for the 'island', and 'sex' columns and follow the steps.

Import OnehotEncoder and LabelEncoder.
Initialize the features encoder object.
Encode the categorical feature columns using the feature_enc object.
Initialize the target encoder object.
Encode the target using the label_enc object.

Lösning

Byt till skrivbordet för praktisk övningFortsätt där du är med ett av alternativen nedan

Var allt tydligt?

Tack för dina kommentarer!

Avsnitt 2. Kapitel 8

single

Fråga AI

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

To summarize the previous three chapters, here is a table showing what encoder you should use:

In this challenge, you have the penguins dataset file (with no missing values). You need to deal with all the categorical values, including the target ('species' column).

Here is the reminder of the data you will work with:


              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.csv')

print(df.head())

Keep in mind that 'island' and 'sex' are categorical features and 'species' is a categorical target.

Uppgift

Swipe to start coding

Encode all the categorical values. For this, you need to choose the correct encoder for the 'island', and 'sex' columns and follow the steps.

Import OnehotEncoder and LabelEncoder.
Initialize the features encoder object.
Encode the categorical feature columns using the feature_enc object.
Initialize the target encoder object.
Encode the target using the label_enc object.

Lösning

Byt till skrivbordet för praktisk övningFortsätt där du är med ett av alternativen nedan

Var allt tydligt?

Tack för dina kommentarer!

Avsnitt 2. Kapitel 8

single

Svep för att visa menyn

To summarize the previous three chapters, here is a table showing what encoder you should use:

In this challenge, you have the penguins dataset file (with no missing values). You need to deal with all the categorical values, including the target ('species' column).

Here is the reminder of the data you will work with:


              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.csv')

print(df.head())

Keep in mind that 'island' and 'sex' are categorical features and 'species' is a categorical target.

Uppgift

Swipe to start coding

Encode all the categorical values. For this, you need to choose the correct encoder for the 'island', and 'sex' columns and follow the steps.

Import OnehotEncoder and LabelEncoder.
Initialize the features encoder object.
Encode the categorical feature columns using the feature_enc object.
Initialize the target encoder object.
Encode the target using the label_enc object.

Lösning

Byt till skrivbordet för praktisk övningFortsätt där du är med ett av alternativen nedan

Var allt tydligt?

Tack för dina kommentarer!

Challenge: Encoding Categorical Variables

Lösning

Awesome!

Challenge: Encoding Categorical Variables

Lösning

Awesome!

Challenge: Encoding Categorical Variables

Lösning