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Encode Categorical Variables | Preprocessing Data with Scikit-learn
ML Introduction with scikit-learn
course content

Course Content

ML Introduction with scikit-learn

ML Introduction with scikit-learn

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

bookEncode Categorical Variables

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

ColumnEncoder
X, ordinal valuesOrdinalEncoder
X, nominal valuesOneHotEncoder
yLabelEncoder

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

Here 'island' and 'sex' are categorical features and 'species' is a categorical target

Task

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 encoder for features.
  2. Initialize the features encoder object.
  3. Fit and transform the categorical feature columns using the feature_enc object.
  4. Fit and transform the target using LabelEncoder.

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Section 2. Chapter 8
toggle bottom row

bookEncode Categorical Variables

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

ColumnEncoder
X, ordinal valuesOrdinalEncoder
X, nominal valuesOneHotEncoder
yLabelEncoder

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

Here 'island' and 'sex' are categorical features and 'species' is a categorical target

Task

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 encoder for features.
  2. Initialize the features encoder object.
  3. Fit and transform the categorical feature columns using the feature_enc object.
  4. Fit and transform the target using LabelEncoder.

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your feedback!

Section 2. Chapter 8
toggle bottom row

bookEncode Categorical Variables

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

ColumnEncoder
X, ordinal valuesOrdinalEncoder
X, nominal valuesOneHotEncoder
yLabelEncoder

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

Here 'island' and 'sex' are categorical features and 'species' is a categorical target

Task

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 encoder for features.
  2. Initialize the features encoder object.
  3. Fit and transform the categorical feature columns using the feature_enc object.
  4. Fit and transform the target using LabelEncoder.

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your feedback!

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

ColumnEncoder
X, ordinal valuesOrdinalEncoder
X, nominal valuesOneHotEncoder
yLabelEncoder

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

Here 'island' and 'sex' are categorical features and 'species' is a categorical target

Task

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 encoder for features.
  2. Initialize the features encoder object.
  3. Fit and transform the categorical feature columns using the feature_enc object.
  4. Fit and transform the target using LabelEncoder.

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Section 2. Chapter 8
Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
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