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Pipeline for song genre prediction: I

Now it's time to build a pipeline. It will contain steps to impute missing values using the mean for each feature and build a KNN model for the classification of song genre.

The modified music_df dataset that you created in the previous exercise has been preloaded for you, along with KNeighborsClassifier and train_test_split.

This exercise is part of the course

Supervised Learning with scikit-learn

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Exercise instructions

  • Import SimpleImputer and Pipeline.
  • Instantiate an imputer.
  • Instantiate a KNN classifier with three neighbors.
  • Create steps, a list of tuples containing the imputer variable you created, called "imputer", followed by the knn model you created, called "knn".

Hands-on interactive exercise

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

# Import modules
____
____

# Instantiate an imputer
imputer = ____()

# Instantiate a knn model
knn = ____

# Build steps for the pipeline
steps = [("____", ____), 
         ("____", ____)]
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