README.md

---
title: "Death Legion Fraud Detection"
emoji: "🔥"
colorFrom: "red"
colorTo: "red"
sdk: "gradio"
sdk_version: "4.19.0"
python_version: "3.10"
app_file: "app.py"
pinned: false
---

# 🔥 Death Legion Fraud Detection System

<p align="center">
<img src="https://img.shields.io/badge/Death%20Legion-Powered-red?style=for-the-badge&logo=skull" alt="Death Legion">
<img src="https://img.shields.io/badge/Best%20Teams-Elite-gold?style=for-the-badge&logo=crown" alt="Best Teams">
<img src="https://img.shields.io/badge/AUPRC-0.8177-success?style=for-the-badge" alt="AUPRC">
<img src="https://img.shields.io/badge/Model%20Size-6.12MB-blue?style=for-the-badge" alt="Size">
</p>

## 🎯 Overview

Welcome to the **Death Legion Fraud Detection System** - a state-of-the-art machine learning solution for real-time credit card fraud detection. Built by the **Best Teams** with cutting-edge Random Forest technology, this model achieves exceptional performance on highly imbalanced financial datasets.

## 🚀 Deploy This Space

<p align="center">
<a href="https://huggingface.co/spaces/Pnny13/fraud-detection-space?duplicate=true">
<img src="https://img.shields.io/badge/🚀%20Deploy%20to%20HF-Spaces-blue?style=for-the-badge" alt="Deploy to Hugging Face">
</a>
</p>

## 🎮 Use This Model

<p align="center">
<a href="https://huggingface.co/Pnny13/fraud-detection-model">
<img src="https://img.shields.io/badge/🤗%20Use%20This%20Model-Hugging%20Face-yellow?style=for-the-badge" alt="Use This Model">
</a>
</p>

## ⚡ Key Features

- **🛡️ Advanced Random Forest Architecture**: 500 estimators with optimized depth
- **📊 Superior Performance**: AUPRC 0.8177 on imbalanced fraud data
- **🔒 Secure Safetensors Format**: Fast, safe model serialization
- **⚡ Real-time Inference**: Sub-millisecond prediction latency
- **🎯 Imbalanced Data Optimized**: Precision-Recall focused evaluation

## 📈 Performance Metrics

| Metric | Score | Status |
|--------|-------|--------|
| **AUPRC** | 0.8177 | ✅ Excellent |
| **Precision** | 0.8182 | ✅ High |
| **Recall** | 0.8265 | ✅ Strong |
| **F1-Score** | 0.8223 | ✅ Balanced |

## 🏗️ Model Architecture

```
Random Forest Classifier
├── Estimators: 500
├── Max Depth: 25
├── Features: 30 (V1-V28 + Time + Amount)
├── Classes: 2 (Legitimate, Fraudulent)
└── Format: Safetensors (6.12 MB)
```

## 📖 How to Use

### Quick Start

1. **Access the Live Demo**: Visit [https://huggingface.co/spaces/Pnny13/fraud-detection-space](https://huggingface.co/spaces/Pnny13/fraud-detection-space)

2. **Enter Transaction Features**: Input the 30 features (V1-V28, Time, Amount) from your credit card transaction

3. **Get Instant Prediction**: The system will return:
   - Fraud probability score (0-100%)
   - Binary classification (Fraud/Legitimate)
   - Recommendation for action

### Programmatic Usage

```python
from safetensors.numpy import load_file
import numpy as np

# Load model from Hugging Face Hub
from huggingface_hub import hf_hub_download
model_path = hf_hub_download(
    repo_id="Pnny13/fraud-detection-model",
    filename="fraud_detector.safetensors"
)

# Load and predict
tensors = load_file(model_path)
# ... use model for predictions
```

## 📁 Repository Structure

```
Pnny13/fraud-detection-space/
├── app.py              # Gradio application
├── requirements.txt    # Python dependencies
└── README.md           # Documentation

Pnny13/fraud-detection-model/
├── fraud_detector.safetensors  # Trained model
├── scaler.joblib               # Feature scaler
├── inference.py                # Prediction script
└── README.md                   # Model documentation
```

## 🔬 Technical Details

### Dataset
- **Source**: [Kaggle - Credit Card Fraud Detection](https://www.kaggle.com/datasets/mlg-ulb/creditcardfraud)
- **Transactions**: 284,807 total
- **Fraud Cases**: 492 (0.172% - highly imbalanced)
- **Features**: 30 PCA-transformed features + Time + Amount

### Preprocessing
- **Robust Scaling**: Applied to Time and Amount features
- **Feature Engineering**: PCA components V1-V28
- **Class Balancing**: Optimized for precision-recall tradeoff

### Training Configuration
```python
RandomForestClassifier(
    n_estimators=500,
    max_depth=25,
    class_weight='balanced_subsample',
    random_state=42,
    n_jobs=-1
)
```

## 🎮 Live Demo

Try the live interactive demo at: **https://huggingface.co/spaces/Pnny13/fraud-detection-space**

## 🤝 Credits

<p align="center">
<strong>Powered by Death Legion</strong><br>
<em>Elite Machine Learning Division</em>
</p>

<p align="center">
<strong>Best Teams Collaboration</strong><br>
<em>Excellence in AI Engineering</em>
</p>

## 📜 License

This model is released under the MIT License. Use responsibly for fraud detection and financial security applications.

## 🔗 Links

- 🤗 Model: https://huggingface.co/Pnny13/fraud-detection-model
- 🚀 Space: https://huggingface.co/spaces/Pnny13/fraud-detection-space
- 📊 Dataset: https://www.kaggle.com/datasets/mlg-ulb/creditcardfraud

<p align="center">
<sub>Built with 🔥 by Death Legion | Best Teams Elite Division</sub>
</p>