Synthetic Data Generation using AI Models

This project showcases how AI models can be used to generate synthetic data, which can be useful for augmenting datasets, improving machine learning model performance, and overcoming data scarcity issues.

Features

• Generates high-quality synthetic data that mimics real-world data distributions.
• Supports various data types including numerical, categorical, and time series data.
• Customizable to generate data based on specific distributions or constraints.
• Includes privacy-preserving techniques to ensure data security.

Installation

To set up the Synthetic Data Generation system on your local machine, follow these steps:

1. Clone the Repository

   git clone https://github.com/username/synthetic-data-generator.git
   cd synthetic-data-generator

2. Set Up Virtual Environment (Optional but Recommended)

   python3 -m venv venv
   source venv/bin/activate   # On Windows: venv\Scripts\activate

3. Install Required Packages

   pip install -r requirements.txt

Usage

To generate synthetic data, run the main script and specify the data type and parameters:

python generate_synthetic_data.py --type "numerical" --rows 1000

Customize the parameters to generate data that fits your specific needs.

Results

The generated data will be outputted in a format that matches the desired specifications, providing a valuable resource for testing, model training, and data analysis without exposing sensitive information.