Data-Driven Feature Engineering

The previous section dealt with business-driven feature engineering. In addition to features we can derive from the business perspective, it would also be imperative to transform data through feature engineering from the perspective of data structures. We will look into different methods of identifying data structures and take a peek into some data transformation techniques.

A Quick Peek at Data Types and a Descriptive Summary

Looking at the data types such as categorical or numeric and then deriving summary statistics is a good way to take a quick peek into data before you do some of the downstream feature engineering steps. Let's take a look at an example from our dataset:

# Looking at Data types
print(bankData.dtypes)
# Looking at descriptive statistics
print(bankData.describe())

You should get the following output:

Figure 3.28: Output showing the different data types in the dataset

In the preceding output...