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:
This exercise is part of the course
Practicing Machine Learning Interview Questions in Python
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Subset features
numeric_cols = ____.____(include=[____.____])
categoric_cols = ____.____(include=[____])