https://github.com/ysayaovong/credit_card_fraud_detection

A project leveraging Generative Adversarial Networks (GANs) to generate synthetic data for credit card fraud detection, tackling data imbalance and enabling effective machine learning model training.
https://github.com/ysayaovong/credit_card_fraud_detection

credit-card-fraud-detection data-imbalance deep-learning gans machine-learning python synthetic-data

Last synced: 8 months ago
JSON representation

A project leveraging Generative Adversarial Networks (GANs) to generate synthetic data for credit card fraud detection, tackling data imbalance and enabling effective machine learning model training.

• Host: GitHub
• URL: https://github.com/ysayaovong/credit_card_fraud_detection
• Owner: YSayaovong
• Created: 2024-12-12T13:40:28.000Z (10 months ago)
• Default Branch: main
• Last Pushed: 2024-12-12T13:51:27.000Z (10 months ago)
• Last Synced: 2024-12-27T12:13:31.894Z (10 months ago)
• Topics: credit-card-fraud-detection, data-imbalance, deep-learning, gans, machine-learning, python, synthetic-data
• Language: Jupyter Notebook
• Homepage:
• Size: 3.99 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

          
# Credit Card Fraud Detection using GANs

![Credit Card Fraud](images/credit_card_fraud.png)

## Project Overview

This project implements a Generative Adversarial Network (GAN) to detect and analyze fraudulent credit card transactions. GANs are used to generate synthetic fraudulent data points, enabling a more balanced dataset for training machine learning models. The project demonstrates the power of GANs in combating real-world issues like fraud detection where data imbalance is a significant challenge.

## Accomplishments

1. **Built and Trained a GAN**:

   - Created a generator to produce synthetic fraudulent data points.

   - Designed a discriminator to distinguish between real and synthetic data.

   - Combined the generator and discriminator to form a complete GAN model.

2. **Visualized Synthetic Data**:

   - Used PCA to reduce dimensionality and visually compare synthetic and real data.

   - Highlighted the differences and similarities between synthetic and real data distributions.

3. **Monitored and Evaluated GAN Performance**:

   - Monitored the GAN's performance during training to ensure meaningful synthetic data generation.

   - Compared individual feature distributions of synthetic and real data.

4. **Analyzed Data with Plots**:

   - Generated interactive and static visualizations to showcase feature distributions and GAN outputs.

---

## Key Components

### GAN Architecture

The GAN consists of:

- **Generator**: A neural network designed to generate synthetic fraudulent data points.

- **Discriminator**: A neural network trained to distinguish between real and synthetic data.

### Training and Evaluation

- The GAN was trained using a dataset of credit card transactions.

- The trained generator was used to produce synthetic fraudulent data, which was then compared against the real data.

### Visualizations

The project includes various visualizations for evaluating the model's performance.

#### PCA Visualization of Real vs Synthetic Data

![Credit Card Fraud PCA](images/credit_card_fraud_2.png)

#### Feature Distribution Comparison

Feature distributions of real and synthetic data are compared using histogram plots to ensure the generator produces realistic outputs.

---

## Tools and Technologies Used

- **Programming Language**: Python

- **Libraries**: TensorFlow, Keras, NumPy, Pandas, Matplotlib, Seaborn, Plotly

- **Machine Learning Techniques**: Generative Adversarial Networks (GANs), PCA