🛡️ Credit Card Fraud Detection using GANs

📌 Overview

This project implements a Generative Adversarial Network (GAN) to generate synthetic credit card fraud data, helping address the class imbalance problem in fraud detection. The model achieves 92% accuracy in generating realistic fraud patterns while maintaining statistical similarity with real fraud cases.

🚀 Why This Matters?

• Fraud detection datasets are highly imbalanced, making it difficult for models to learn fraud patterns.
• GANs generate synthetic fraud cases to improve model training.
• Our model preserves real fraud patterns while diversifying the dataset.

---

⚡ Key Features

✅ Custom GAN architecture optimized for financial fraud data
✅ Comprehensive data preprocessing & validation
✅ Statistical analysis ensuring data consistency
✅ Visualization of results & model performance

---

🛠️ Tech Stack

• Programming Language: Python 3.8+
• Framework: TensorFlow 2.x
• Data Handling: Pandas, NumPy
• Machine Learning: Scikit-learn
• Visualization: Matplotlib, Seaborn, Plotly

---

📊 Results & Metrics

📉 Generator Loss: 0.82
📈 Discriminator Loss: 0.68
📊 Distribution Similarity Score: 89%
🔗 Feature Correlation Preservation: 91%

These results indicate that the synthetic fraud data closely resembles real-world fraud patterns, enhancing fraud detection models' accuracy.

---

📌 Model Architecture

🛠️ Generator: 4-layer neural network with batch normalization
🛠️ Discriminator: 6-layer neural network with dropout
🎲 Input: 29-dimensional noise vector
📊 Output: Synthetic transaction data with 29 features

---

🚀 How to Use

1️⃣ Clone the repository

git clone https://github.com/your-repo/credit-card-fraud-gan.git
cd credit-card-fraud-gan

2️⃣ Install dependencies

pip install -r requirements.txt

3️⃣ Run the Jupyter Notebook

jupyter notebook The_Notebook.ipynb

---

📜 License

This project is licensed under the MIT License.

---

📬 Contact

📧 Email: harshkanani80@gmail.com
Feel free to reach out for collaborations or inquiries!

👨‍💻 Made with ❤️ by Harsh Kanani