https://github.com/adn26/fraud-detection-system
A machine learning-based system for detecting potentially fraudulent transactions in real-time.
https://github.com/adn26/fraud-detection-system
Last synced: about 2 months ago
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A machine learning-based system for detecting potentially fraudulent transactions in real-time.
- Host: GitHub
- URL: https://github.com/adn26/fraud-detection-system
- Owner: adn26
- License: mit
- Created: 2025-04-12T18:22:53.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-04-15T08:27:47.000Z (about 1 year ago)
- Last Synced: 2025-12-26T15:33:01.347Z (7 months ago)
- Language: Python
- Homepage:
- Size: 144 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Fraud Detection System
A machine learning-based system for detecting potentially fraudulent transactions in real-time.
## Overview
This project implements a fraud detection system using machine learning to identify suspicious transactions. The system analyzes various features of transactions, including:
- Transaction amount and timing
- Customer transaction history
- Terminal transaction history
- Temporal patterns
## Demo
Here are examples of how the system evaluates transactions:
### Input Interface
*The user interface for entering transaction details and historical statistics*
### Example Results
#### Legitimate Transaction Example
*A normal transaction with low fraud probability and no risk factors*
#### Fraudulent Transaction Example
*A suspicious transaction flagged with multiple risk factors showing:*
- High transaction amount (>220)
- Unusual amount for the customer
- Unusual amount for the terminal
## Features
- Real-time fraud detection
- Web-based interface using Streamlit
- Model training and evaluation
- Transaction testing capabilities
- Feature importance visualization
- Risk factor analysis
## Project Structure
```
fraud_detection/
├── app.py # Streamlit web application
├── fraud_detection.py # Main training and model code
├── test_fraud_detection.py # Transaction testing script
├── convert_to_csv.py # Data conversion utility
├── requirements.txt # Python dependencies
└── README.md # Project documentation
```
## Setup
1. Clone the repository:
```bash
git clone https://github.com/yourusername/fraud_detection.git
cd fraud_detection
```
2. Create and activate a virtual environment:
```bash
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
```
3. Install dependencies:
```bash
pip install -r requirements.txt
```
## Usage
### Training the Model
1. Place your transaction data in the `data` directory
2. Run the training script:
```bash
python fraud_detection.py
```
### Testing Transactions
1. Use the test script to check individual transactions:
```bash
python test_fraud_detection.py
```
### Web Interface
1. Start the Streamlit app:
```bash
streamlit run app.py
```
2. Open your browser and navigate to `http://localhost:8501`
## Features Used for Detection
- Transaction Amount
- Log-transformed Amount
- Time of Transaction (Hour, Day, Month)
- Customer Statistics:
- Mean Transaction Amount
- Standard Deviation
- Transaction Count
- Terminal Statistics:
- Mean Transaction Amount
- Standard Deviation
- Transaction Count
## Model Performance
The system uses a Random Forest classifier with the following characteristics:
- Handles class imbalance
- Provides probability estimates
- Evaluates multiple risk factors
- Visualizes feature importance
## Contributing
1. Fork the repository
2. Create a feature branch
3. Commit your changes
4. Push to the branch
5. Create a Pull Request
## License
This project is licensed under the MIT License - see the LICENSE file for details.
## Acknowledgments
- Data preprocessing techniques
- Feature engineering approaches
- Machine learning model implementation
## Dataset Description
The dataset contains simulated transaction data with the following columns:
- TRANSACTION_ID: Unique identifier for the transaction
- TX_DATETIME: Date and time of the transaction
- CUSTOMER_ID: Unique identifier for the customer
- TERMINAL_ID: Unique identifier for the merchant terminal
- TX_AMOUNT: Amount of the transaction
- TX_FRAUD: Binary variable (0 for legitimate, 1 for fraudulent)
## Fraud Patterns
The system detects fraud based on three main patterns:
1. Transactions with amount > 220
2. Transactions from compromised terminals
3. Unusual spending patterns from compromised customer accounts