Machine learning models depend heavily on data. Acquiring enough suitable real-world data often involves difficulties related to availability, privacy regulations, or class imbalance. This chapter introduces synthetic data, artificially generated information used as an alternative or supplement.

We will define what synthetic data means in this context, examine the primary reasons for its generation, and compare it directly against real data, noting differences, advantages, and drawbacks. You will also learn essential terminology used in this area, understand the benefits synthetic data can offer, and recognize its fundamental limitations. By the end of this chapter, you will have a clear understanding of the basic ideas behind synthetic data and its place in machine learning projects.