Cost: the data collection takes time and resources.Examples: credit fraud detection, car crashes, and cancer data. Rare Cases: we cannot wait for the rare event to occur and collect real-world data.For example: in image classifiers, we use the shearing, shifting, and rotating of images to increase the size of the dataset and improve model accuracy. Model Performance: generated synthetics data can improve model performance.Testing database, UI, and AI applications on synthetics data is more cost-efficient and secure. Testing: application testing on real-world data is expensive.It will help us avoid cyber and black-box attacks where models infer the details of training data. 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. fake: the name of the unreal for which output is to be generated, such as an address, an email, or text : optional arguments to send to the fake, for instance, the profile, takes a list of optional comma-separated field names as the first argument.Synthetic data is computer-generated data that is similar to real-world data.It’s important to note that this is the import path for the package that contains your Provider class, not the custom Provider class. -i shows a list of additional custom providers to use.-s SEP: produces the needed separator after each generated output.-r REPEAT: This option generates a set count of output values.-o FILENAME: ensures that the output is redirected to the given filename.- version: displays the version number of the program. -h, - show help or displays a help message.When installed in your environment, faker is the script in development, you may use python -m faker instead. You can type the code directly into the command prompt. You can also use the Faker package from the command line after installing it. We’ll start by configuring Faker with Django and then looking at producing data.Ĭurrency-Related Dummy Data Using the Faker Package on the Command-Line This article will utilize Faker in Django to make some early data for our database. Using the Random module from the Numpy packageįaker is one of the Python libraries that helps you create fake data.
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