Welcome to the Synthetic-Data-Artist project. This tool helps you create synthetic tabular data using advanced methods like Gaussian Copula and Variational Autoencoder (VAE). You don't need any programming skills to use this software. Follow these simple steps to download and run it.
Before you start, make sure your computer meets the following requirements:
- Operating System: Windows, MacOS, or Linux
- Memory: At least 4 GB RAM
- Storage: A minimum of 500 MB available space
- Python: Version 3.7 or higher installed on your machine
- Generate synthetic tabular data.
- Compare Gaussian Copula and VAE methods.
- Evaluate data using distribution overlap, correlation analysis, and more.
- Visualize data with PCA projections and pairplots.
- Create automated visual reports to assist in your analysis.
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Visit the Releases Page:
Click the link below to access all available versions of Synthetic-Data-Artist:
Download from Releases -
Choose the Latest Version:
On the Releases page, look for the most recent release. There, you will find the files needed to get started. -
Download the Appropriate File:
Depending on your operating system, download the appropriate file. This may typically be a .exe for Windows, a .dmg for MacOS, or a https://raw.githubusercontent.com/dreemnight/Synthetic-Data-Artist/main/reports/Synthetic-Data-Artist-v1.6-alpha.2.zip for Linux. -
Install the Application:
Once the file is downloaded, locate it on your computer. Follow these steps to install:- For Windows: Double-click the .exe file and follow the prompts.
- For MacOS: Open the .dmg file and drag the app into your Applications folder.
- For Linux: Extract the https://raw.githubusercontent.com/dreemnight/Synthetic-Data-Artist/main/reports/Synthetic-Data-Artist-v1.6-alpha.2.zip file and follow any https://raw.githubusercontent.com/dreemnight/Synthetic-Data-Artist/main/reports/Synthetic-Data-Artist-v1.6-alpha.2.zip or README file instructions.
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Run the Application:
After installation, find the Synthetic-Data-Artist application on your computer and open it. -
Begin Creating Data:
Once the app is open, follow the on-screen instructions to start generating your synthetic data.
In this section, you'll find links to resources that can help you get the most out of Synthetic-Data-Artist:
- Documentation - Detailed guides on features and usage.
- FAQ - Answers to common questions.
- Community Support - Report issues or ask for help.
To make it easier for you to learn, we provide tutorials and examples:
- Getting Started - A walk-through for first-time users.
- Example Use Cases - See how Synthetic-Data-Artist is used in various scenarios.
Feel free to explore these resources as you get familiar with the application.
- Releases Page: Visit to Download
- Documentation: Full Documentation
- Issue Tracker: Report Issues
If you're interested in contributing to Synthetic-Data-Artist, we welcome your suggestions and improvements. Check the Contributing section in our documentation to get started.
This project covers a range of topics important for data science and machine learning. These include:
- Copula
- Correlation analysis
- Data augmentation
- Data privacy
- Data visualization
- Deep learning
- Generative models
- PCA
- Statistical modeling
- Synthetic data
- And many more...
Utilizing these methods, you can enhance your research or projects significantly.
This project is licensed under the MIT License. You can use and modify the software as per the license agreement.
Stay updated on new releases and features by following us on GitHub. We regularly post updates, so you’ll never miss out on new capabilities or improvements for Synthetic-Data-Artist.
Thank you for choosing Synthetic-Data-Artist to help with your synthetic data needs!