Exercise

Z-score standardization

In the last exercise, you calculated the Z-score to address outliers. In a machine learning interview, another question might be where else Z-scores are used. They are often used for scaling your data prior to creating a model.

In this exercise you'll use a function from sklearn.preprocessing that was introduced in the video lesson to standardize the numeric feature columns in the loan_data dataset. Recall that this scales the data so that it has a mean of 0 and standard deviation of 1.

The sklearn.preprocessing module has already been imported for you.

Pipeline snapshot:

Machine learning pipeline

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Create a subset of the numeric and categorical columns in loan_data.