Introduction

We learned about the process of machine learning in Chapter 1, An Introduction to Machine Learning, and looked at the different ways to process data in Chapter 2, Data Cleaning and Pre-processing. In this chapter, we will delve deep into the feature engineering process. Feature engineering is a process in which we select the attributes that are related to the target field in our dataset. The selection is made using techniques such as correlation analysis, Principal Component Analysis (PCA), and other techniques. During this process, new features can also be generated that are meaningful and add information to our dataset. In addition to this, we can generate statistics of existing numeric fields as features, as they contain statistical information about the fields or attributes.

In this chapter, we will learn how to create features for date variables, time series data, strings, and numeric variables, and explore text features. Furthermore, we will look at the implementation...