Synthetic data is a cost-effective and efficient way to generate data for training machine learning models. Unlike real data, which can be expensive and time-consuming to collect, synthetic data is generated by algorithms and is therefore readily available. In addition, synthetic data is not subject to the same privacy concerns as real data, as it is fully anonymous and cannot be traced back to individuals. Synthetic data is also more flexible than real data, as it can be generated to include edge cases and rare events that might not be captured in real-world data sets. As a result, synthetic data help our machine learning models to achieve higher levels of accuracy.