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credit-card-fraud-detection
Jassiko6/credit-card-fraud-detection
credit-card-fraud-detection is a machine learning model from Jassiko6. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
A comparative study of unsupervised anomaly detection techniques using PyTorch. This project evaluates different Autoencoder architectures and PCA to identify fraudulent transactions by measuring reconstruction error.
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Updated Jan 22, 2026
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At a glance
- Access
- Public
- Created
- Jan 22, 2026
- Updated
- Jan 22, 2026
- SHA
- 336a1f60
Model card
From the Hugging Face model README
Credit Card Fraud Detection - Autoencoder
A comparative study of unsupervised anomaly detection techniques using PyTorch. This project evaluates different Autoencoder architectures and PCA to identify fraudulent transactions by measuring reconstruction error.
Authors
Project Overview
In the financial industry, fraudulent transactions are rare events (anomalies). This dataset contains transactions made by European cardholders, where frauds make up a small percentage of all transactions.
- An Autoencoder is trained to compress and then reconstruct normal transactions with high precision.
- When the model encounters a fraudulent transaction (which it hasn't seen before), it fails to reconstruct it accurately.
- A high Mean Squared Error (MSE) between the input and the output indicates a high probability of fraud.
Model Architectures
| Model | Architecture | Key Characteristic |
|---|---|---|
| Basic AE | 30 → 15 → 7 | Standard bottleneck design to learn essential features. |
| Deep AE | 30 → 20 → 10 → 5 | Deeper hierarchy with Dropout (0.2) to prevent overfitting. |
| Sparse AE | 30 → 15 → 7 | Uses L1 Regularization to force the model to use only the most important neurons. |
| PCA | Linear Baseline | Principal Component Analysis with 10 components for statistical comparison. |
Tech Stack
- Deep Learning: PyTorch
- Data Manipulation: Pandas, NumPy
- Machine Learning: Scikit-Learn (StandardScaler, PCA, Train-Test Split)
- Visualization: Matplotlib
- Dataset Handling: KaggleHub
Installation & Usage
-
Clone the repository:
git clone https://github.com/Jassiko6/credit-card-fraud-detection cd credit-card-fraud-detection -
Install dependencies:
pip install torch pandas numpy matplotlib scikit-learn kagglehub -
Run the script:
python main.py
- Created
- Jan 22, 2026
- Updated
- Jan 22, 2026