This project demonstrates how to use a Generative Adversarial Network (GAN) to generate synthetic data resembling a given real dataset. The purpose of generating synthetic data is to create data samples that are not present in the original dataset but have similar characteristics. This can be useful for data augmentation, anonymization, and other purposes.
In this project, we have used the Keras library to implement a GAN. The GAN consists of two neural networks, a generator, and a discriminator. The generator creates synthetic data samples, while the discriminator tries to distinguish between real and synthetic data samples.
The dataset used in this project consists of two features: Gini Index and Income per Capita. The goal is to generate synthetic data samples that resemble the distribution of the real data.
Before running the code, make sure to install the required Python libraries:
pip install numpy
pip install matplotlib
pip install tensorflow
To run the code, simply execute the Jupyter notebook:
jupyter notebook gan_synthetic_data.ipynb
The project is structured as follows:
- 'gan-synthetic-data-generation.py': Python script containing the code to train the GAN and generate synthetic data.
- 'gan-synthetic-data-generation.ipynb': Jupyter notebook containing the same code as the Python script but in a notebook format.
- 'README.md': This file, which provides an overview of the project and instructions for running the code.
The GAN is implemented using the Keras library with a TensorFlow backend. The generator and discriminator networks are both simple feedforward neural networks with multiple layers.
The generator takes random noise as input and produces synthetic data samples. The discriminator takes both real and synthetic data samples as input and tries to classify them as either real or synthetic.
The training process involves alternating between training the generator and discriminator. The generator is trained to create data samples that the discriminator incorrectly classifies as real, while the discriminator is trained to improve its ability to distinguish between real and synthetic data samples.
After training, the generator is used to create a set of synthetic data samples, which are then visualized alongside the real data samples to evaluate the performance of the GAN.
The resulting synthetic data samples are plotted alongside the real data samples to visualize the performance of the GAN. The synthetic data samples should resemble the distribution of the real data, indicating that the GAN has successfully learned to generate synthetic data with similar characteristics as the real data.
This project is licensed under the MIT License - see the LICENSE file for details.