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One-hot encoding and dummy variables

To use categorical variables in a machine learning model, you first need to represent them in a quantitative way. The two most common approaches are to one-hot encode the variables using or to use dummy variables. In this exercise, you will create both types of encoding, and compare the created column sets. We will continue using the same DataFrame from previous lesson loaded as so_survey_df and focusing on its Country column.

This exercise is part of the course

Feature Engineering for Machine Learning in Python

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Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Convert the Country column to a one hot encoded Data Frame
one_hot_encoded = ____(____, ____=['Country'], prefix='OH')

# Print the columns names
print(one_hot_encoded.columns)
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