Encoding binary features

Recasting data types is an important part of data preprocessing. In this exercise you will assign the values 1 to 'yes' and 0 to 'no' to the 'Vmail_Plan' and 'Churn' features, respectively.

You saw two approaches to doing this in the video - one using pandas, and the other using scikit-learn. For straightforward tasks like this, sticking with pandas is recommended, so that's what we'll do in this exercise. If you're trying to build machine learning pipelines, on the other hand - which is beyond the scope of this course - you can explore using LabelEncoder(). When doing data science, it's important to be aware that there is always more than one way to accomplish a task, and you need to pick the one that is most effective for your application.

This exercise is part of the course

Marketing Analytics: Predicting Customer Churn in Python

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Exercise instructions

Replace 'no' with 0 and 'yes' with 1 in the 'Vmail_Plan' column of telco.
Do the same for the 'Churn' column.

Hands-on interactive exercise

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

# Replace 'no' with 0 and 'yes' with 1 in 'Vmail_Plan'
telco['Vmail_Plan'] = telco['____'].____(____)

# Replace 'no' with 0 and 'yes' with 1 in 'Churn'
telco['Churn'] = ____

# Print the results to verify
print(telco['Vmail_Plan'].head())
print(telco['Churn'].head())

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