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Dropping missing data

Over the next three exercises, you are going to tidy the music_df dataset. You will create a pipeline to impute missing values and build a KNN classifier model, then use it to predict whether a song is of the "Rock" genre.

In this exercise specifically, you will drop missing values accounting for less than 5% of the dataset, and convert the "genre" column into a binary feature.

This exercise is part of the course

Supervised Learning with scikit-learn

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Hands-on interactive exercise

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

# Print missing values for each column
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