Ecommerce Synthetic Data Generation

This project is designed to generate realistic data for an electronics e-commerce retail store. The data is generated using a Jupyter notebook (e_commerce_synthetic.ipynb), allowing easy modification and execution of the data generation process.

Features

• Realistic Customer Information: The Faker library generates realistic customer information, such as names, addresses, and phone numbers.
• Electronics Products: The script simulates a variety of electronics products, including TVs, smartphones, laptops, and more.
• Order details and Other Information: The script generates realistic orders data, including quantities, prices, and dates. Inventorylog data, and customer service data has also been generated. Reviews have been generated with the help of OpenAI's GPT-4 Model.

Getting Started

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes.

Prerequisites

You need to have Python installed on your machine. You can download Python here.

How to Use

1. Clone this repository to your local machine.

git clone https://github.com/git-GB/ecommerce_datagen.git

2. Navigate to the project directory.

cd ecommerce-datagen

3. Install the required dependencies by running

pip install -r requirements.txt

4. Run the Notebook: Open the e_commerce_synthetic.ipynb notebook in Jupyter and run the cells to generate the data.

Customization

You can customize the data types generated by modifying the e_commerce_synthetic.ipynb notebook. The notebook contains comments explaining what each part of the code does, making it easy to adjust to your needs. I am Currently working on more detailed guidelines.

Built With

• Python
• Pandas
• Numpy
• Faker
• OpenAI API

Authors

• Govind Bhat - E-commerce Store Realistic Data Generation- git-GB

License

This project is licensed under the MIT License - see the LICENSE.md file for details