Case Study: Synthetic Data Generation

Synthetic data generation is a crucial skill in various fields, including data science, machine learning, and software testing. By creating realistic yet artificial datasets, we can overcome data privacy concerns, facilitate model development, and enhance the robustness of our applications.

In this hands-on example, you’ll learn how to use the Faker library to generate synthetic datasets with diverse characteristics.