Imputing missing data in a ML Pipeline I

As you've come to appreciate, there are many steps to building a model, from creating training and test sets, to fitting a classifier or regressor, to tuning its parameters, to evaluating its performance on new data. Imputation can be seen as the first step of this machine learning process, the entirety of which can be viewed within the context of a pipeline. Scikit-learn provides a pipeline constructor that allows you to piece together these steps into one process and thereby simplify your workflow.

You'll now practice setting up a pipeline with two steps: the imputation step, followed by the instantiation of a classifier. You've seen three classifiers in this course so far: k-NN, logistic regression, and the decision tree. You will now be introduced to a fourth one - the Support Vector Machine, or SVM. For now, do not worry about how it works under the hood. It works exactly as you would expect of the scikit-learn estimators that you have worked with previously, in that it has the same .fit() and .predict() methods as before.

Import Imputer from sklearn.preprocessing and SVC from sklearn.svm. SVC stands for Support Vector Classification, which is a type of SVM.
Setup the Imputation transformer to impute missing data (represented as 'NaN') with the 'most_frequent' value in the column (axis=0).
Instantiate a SVC classifier. Store the result in clf.
Create the steps of the pipeline by creating a list of tuples:
- The first tuple should consist of the imputation step, using imp.
- The second should consist of the classifier.

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Exercise

Imputing missing data in a ML Pipeline I

Instructions