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
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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