Data analytics .This PPT tells about the clear view of generating synthetic transaction data

This PPT tells about the clear view of generating synthetic transaction data

1. GENERATING SYNTHETIC TRANSACTION DATA Presented by: Jayasri J

2. TITILE OVERVIEW What is Synthetic Data Generation ? Why Use Synthetic Data? Applications of Synthetic Data How Does Synthetic Data Generation Work? Conclusion

3. What is Synthetic Data Generation? Synthetic data Generation involves creating artificial data that mimics the statistical properties and patterns of real-world data. It is created using algorithms and models to replicate the statistical properties of actual data without directly copying it. This approach is particularly beneficial in scenarios where real data is scarce, expensive, or sensitive due to privacy concerns

4. Why Use Synthetic Data? Synthetic data is required for several reasons, primarily related to overcoming the limitations and challenges associated with real-world data. Overcoming Data Scarcity : In many fields, obtaining sufficient real-world data can be challenging due to privacy concerns, high costs, or logistical constraints. Scalability : Synthetic data can be generated in large volumes, facilitating the training of complex mode ls that require vast amounts of data. Data Diversity : It allows for the creation of diverse datasets that include rare events or anomalies, enhancing the robustness of machine learning models.

5. Applications of Synthetic Data

6. How Does Synthetic Data Generation Work? Data Distribution Estimation The first step in synthetic data generation is to estimate the underlying distribution of the real data. This can be done using statistical models, machine learning models, or deep learning models.
The model learns the distribution of the real data so that it can generate new data points that resemble the real data. Data Sampling Once the model has learned the data distribution, it can sample new data points from this distribution. These data points are synthetic but are statistically similar to the real data.

7. Post-processing In some cases, the synthetic data may require post-processing to ensure that it meets certain constraints or has specific characteristics (e.g., valid values, specific ranges).

8. Conclusion In conclusion, synthetic data offers significant advantages, such as addressing data scarcity, enhancing privacy, and enabling robust testing of models across various scenarios. However, it also presents challenges, including potential issues with realism, quality, and complexity in generation.