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  3. Feature Engineering for Machine Learning in Python

Exercise

Selecting specific data types

Often a dataset will contain columns with several different data types (like the one you are working with). The majority of machine learning models require you to have a consistent data type across features. Similarly, most feature engineering techniques are applicable to only one type of data at a time. For these reasons among others, you will often want to be able to access just the columns of certain types when working with a DataFrame.

The DataFrame (so_survey_df) from the previous exercise is available in your workspace.

Instructions

100 XP
  • Create a subset of so_survey_df consisting of only the numeric (int and float) columns.
  • Print the column names contained in so_survey_df_num.