You can replace names, emails, and address with synthetic data. We need synthetic data for user privacy, application testing, improving model performance, representing rare cases, and reducing the cost of operation. Why Do We Need to Generate Synthetic Data? In the final part, we will explore the Python Faker library and use it to create synthetic data for testing and maintaining user privacy. In the first part of the tutorial, we will learn about why we need synthetic data, its applications, and how to generate it. Even if you get the data, it will take time and resources to clean and process it for machine learning tasks. For example, bank fraud, breast cancer, self-driving cars, and malware attack data are rare to find in the real world. It is costly to collect and clean real-world data, and in some cases, it is rare. But why are we seeing an upward trend of synthetics data? The typical use of synthetics data in machine learning is self-driving vehicles, security, robotics, fraud protection, and healthcare.Īccording to data from Gartner, by 2024, 60% of data used to develop machine learning and analytical applications will be synthetically generated. It is also valid for situations where data is scarce and unbalanced. In the case of machine learning, we use synthetic data to improve model performance. Using synthetic data can help companies test new applications and protect user privacy. For example, to protect the Personally Identifiable Information (PII) or Personal Health Information (PHI) of the users, companies have to implement data protection strategies. The primary purpose of synthetics data is to increase the privacy and integrity of systems. Synthetic data is computer-generated data that is similar to real-world data.
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