Synthetic Tabular Data Generation for Automotive Manufacturing

This repository contains code and notebooks for generating synthetic tabular data using various generative models, specifically tailored for automotive manufacturing applications. The main focus is on comparing the performance of different algorithms in producing realistic synthetic data.

Table of Contents

• Introduction
• Algorithms
• Getting Started
• Repository Structure
• Usage
• Results
• Contributing
• License

Introduction

Generating synthetic tabular data is crucial for various purposes, such as data augmentation, privacy preservation, and testing machine learning models. This repository explores different generative models and compares their performance in the context of automotive manufacturing.

Algorithms

The following algorithms are implemented and compared in this repository:

1. Generative Adversarial Networks (GANs)
2. Variational Autoencoders (VAEs)
3. Bayesian Networks (BNs)
4. Synthetic Data Vault (SDV) - Gaussian Copula and CTGAN