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  3. Case Study: School Budgeting with Machine Learning in Python

Exercise

Using FunctionTransformer on the main dataset

In this exercise you're going to use FunctionTransformer on the primary budget data, before instantiating a multiple-datatype pipeline in the next exercise.

Recall from Chapter 2 that you used a custom function combine_text_columns to select and properly format text data for tokenization; it is loaded into the workspace and ready to be put to work in a function transformer!

Concerning the numeric data, you can use NUMERIC_COLUMNS, preloaded as usual, to help design a subset-selecting lambda function.

You're all finished with sample data. The original df is back in the workspace, ready to use.

Instructions

100 XP
  • Complete the call to multilabel_train_test_split() by selecting df[NON_LABELS].
  • Compute get_text_data by using FunctionTransformer() and passing in combine_text_columns. Be sure to also specify validate=False.
  • Use FunctionTransformer() to compute get_numeric_data. In the lambda function, select out the NUMERIC_COLUMNS of x. Like you did when computing get_text_data, also specify validate=False.