Notice: This page requires JavaScript to function properly.
Please enable JavaScript in your browser settings or update your browser.
Impara Challenge: Encoding Categorical Variables | Preprocessing Data with Scikit-learn
ML Introduction with scikit-learn
course content

Contenuti del Corso

ML Introduction with scikit-learn

ML Introduction with scikit-learn

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

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.

Compito

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.

  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.

Soluzione

Switch to desktopCambia al desktop per esercitarti nel mondo realeContinua da dove ti trovi utilizzando una delle opzioni seguenti
Tutto è chiaro?

Come possiamo migliorarlo?

Grazie per i tuoi commenti!

Sezione 2. Capitolo 8
toggle bottom row

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.

Compito

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.

  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.

Soluzione

Switch to desktopCambia al desktop per esercitarti nel mondo realeContinua da dove ti trovi utilizzando una delle opzioni seguenti
Tutto è chiaro?

Come possiamo migliorarlo?

Grazie per i tuoi commenti!

Sezione 2. Capitolo 8
Switch to desktopCambia al desktop per esercitarti nel mondo realeContinua da dove ti trovi utilizzando una delle opzioni seguenti
Siamo spiacenti che qualcosa sia andato storto. Cosa è successo?
some-alt