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Exercise

Converting categorical variables

Because sklearn requires numerical features as inputs for models, it is important to encode categorical variables into numerical ones. The most common technique, called "one-hot encoding", is straightforward but has high memory consumption. To that end, you will use the technique of hashing, which maps categorical inputs into numerical ones, for each categorical column.

The pandas module is available as pd in your workspace and the sample DataFrame is loaded as df.

Instructions

100 XP
  • Select the categorical columns by filtering for data type.
  • Apply a hash function over each of the categorical columns.