Metadata-Version: 2.1
Name: ydata-synthetic
Version: 0.2.1
Summary: Synthetic data generation methods with different synthetization methods.
Home-page: https://github.com/ydataai/ydata-synthetic
Author: YData
Author-email: community@ydata.ai
License: https://github.com/ydataai/ydata-synthetic/blob/master/LICENSE
Description: ![](https://img.shields.io/github/workflow/status/ydataai/ydata-synthetic/prerelease)
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        <p align="center"><img width="200" src="https://ydata-demos.s3.eu-central-1.amazonaws.com/Synthetic+Data_2.png" alt="Synthetic Data Logo"></p>
        
        Join us on [![slack](https://img.shields.io/badge/slack-brightgreen.svg?logo=slack)](http://slack.ydata.ai/)
        
        # What is Synthetic Data?
        Synthetic data is artificially generated data that is not collected from real world events. It replicates the statistical components of real data without containing any identifiable information, ensuring individuals' privacy.
        
        # Why Synthetic Data?
        Synthetic data can be used for many applications:
        - Privacy
        - Remove bias
        - Balance datasets
        - Augment datasets
        
        # ydata-synthetic
        This repository contains material related with Generative Adversarial Networks for synthetic data generation, in particular regular tabular data and time-series. 
        It consists in a set of different GANs architectures developed ussing Tensorflow 2.0. An example Jupyter Notebook is included, to show how to use the different architectures.
        
        # Quickstart
        ```
        pip install ydata-synthetic
        ```
        
        ## Examples
        Here you can find usage examples of the package and models to synthesize tabular data.
        
        **Credit Fraud dataset**   [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ydataai/ydata-synthetic/blob/master/examples/regular/gan_example.ipynb)
        
        **Stock dataset** [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ydataai/ydata-synthetic/blob/master/examples/timeseries/TimeGAN_Synthetic_stock_data.ipynb)
        
        # Project Resources
        - Synthetic GitHub: https://github.com/ydataai/ydata-synthetic
        - Synthetic Data Community Slack: [click here to join](http://slack.ydata.ai/)
        
        ### In this repo you can find the following GAN architectures:
        
        #### Tabular data
        - [GAN](https://arxiv.org/abs/1406.2661)
        - [CGAN (Conditional GAN)](https://arxiv.org/abs/1411.1784)
        - [WGAN (Wasserstein GAN)](https://arxiv.org/abs/1701.07875)
        - [WGAN-GP (Wassertein GAN with Gradient Penalty)](https://arxiv.org/abs/1704.00028)
        - [DRAGAN (On Convergence and stability of GANS)](https://arxiv.org/pdf/1705.07215.pdf)
        - [Cramer GAN (The Cramer Distance as a Solution to Biased Wasserstein Gradients)](https://arxiv.org/abs/1705.10743)
        
        #### Sequential data
        - [TimeGAN](https://papers.nips.cc/paper/2019/file/c9efe5f26cd17ba6216bbe2a7d26d490-Paper.pdf)
        
Keywords: data science ydata
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: End Users/Desktop
Classifier: Intended Audience :: Financial and Insurance Industry
Classifier: Intended Audience :: Healthcare Industry
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Telecommunications Industry
Classifier: License :: OSI Approved :: GNU General Public License v3 or later (GPLv3+)
Classifier: Operating System :: POSIX :: Linux
Classifier: Operating System :: MacOS
Classifier: Operating System :: Microsoft :: Windows
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Programming Language :: Python :: Implementation
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Software Development
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Requires-Python: >=3.6, <3.9
Description-Content-Type: text/markdown
