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Challenge: Encoding Categorical Variables | Preprocessing Data with Scikit-learn
ML Introduction with scikit-learn
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Contenido del Curso

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: Encoding Categorical Variables

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

Tarea
test

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

  1. Import the correct encoders for features and target.
  2. Initialize the features encoder object.
  3. Encode the categorical feature columns using the feature_enc object.
  4. Initialize the target encoder object.
  5. Encode the target using the label_enc object.

Switch to desktopCambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones
¿Todo estuvo claro?

¿Cómo podemos mejorarlo?

¡Gracias por tus comentarios!

Sección 2. Capítulo 8
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book
Challenge: Encoding Categorical Variables

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())
copy

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

Tarea
test

Swipe to show code editor

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

  1. Import the correct encoders for features and target.
  2. Initialize the features encoder object.
  3. Encode the categorical feature columns using the feature_enc object.
  4. Initialize the target encoder object.
  5. Encode the target using the label_enc object.

Switch to desktopCambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones
¿Todo estuvo claro?

¿Cómo podemos mejorarlo?

¡Gracias por tus comentarios!

Sección 2. Capítulo 8
Switch to desktopCambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones
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