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
Exercise instructions
- Import
SimpleImputer
andPipeline
. - 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 theknn
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 = [("____", ____),
("____", ____)]