Synthetic Data refers to data that is artificially generated rather than collected from real-world events. This type of data is used in machine learning and data analysis to augment real datasets, particularly when real data is insufficient, expensive, or sensitive. Synthetic data can be created using simulations, generative models, or other data generation techniques, providing a controlled environment to test and train models. It is especially valuable in scenarios involving privacy concerns or where data is difficult to obtain. By increasing the volume and diversity of data, synthetic data helps improve model accuracy and robustness.