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https://github.com/lavkalsi/creditcardfrauddetector
Credit Card Fraud Detector is a React web app that predicts if a credit card transaction is fraudulent using a Python machine learning model. Users can input transaction data, and Flask facilitates communication between the backend and frontend. Backend files are located in the res folder. This app provides simple UI for user interaction.
https://github.com/lavkalsi/creditcardfrauddetector
fraud-detection machine-learning nodejs numpy pandas python reactjs sklearn-library
Last synced: about 1 month ago
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Credit Card Fraud Detector is a React web app that predicts if a credit card transaction is fraudulent using a Python machine learning model. Users can input transaction data, and Flask facilitates communication between the backend and frontend. Backend files are located in the res folder. This app provides simple UI for user interaction.
- Host: GitHub
- URL: https://github.com/lavkalsi/creditcardfrauddetector
- Owner: LavKalsi
- Created: 2024-06-20T07:45:01.000Z (8 months ago)
- Default Branch: main
- Last Pushed: 2024-07-03T14:20:16.000Z (8 months ago)
- Last Synced: 2024-11-19T12:58:01.722Z (3 months ago)
- Topics: fraud-detection, machine-learning, nodejs, numpy, pandas, python, reactjs, sklearn-library
- Language: Jupyter Notebook
- Homepage: https://lavkalsi.github.io/CreditCardFraudDetector
- Size: 672 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Credit Card Fraud Detector
A React web application that predicts if a credit card transaction is fraudulent using a Python machine learning model. Flask is used for communication between the Python backend and the React app.
## Table of Contents
- [Introduction](#introduction)
- [Features](#features)
- [Installation](#installation)
- [Usage](#usage)
- [How It Works](#how-it-works)
- [Backend Details](#backend-details)
- [Contributing](#contributing)
- [License](#license)
- [Contact](#contact)
## Introduction
Credit Card Fraud Detector is a web application built with React for the frontend and a Python machine learning model for the backend. It predicts if a credit card transaction is fraudulent based on the input transaction data.
## Features
- **User-friendly Interface:** Simple interface to input transaction data and predict fraud.
- **Real-time Predictions:** Quickly processes input to provide fraud detection.
- **Machine Learning:** Utilizes a trained machine learning model for accurate predictions.
## Installation
### Prerequisites
- Node.js
- Python 3.x
- pip (Python package installer)
### Frontend Setup
1. Clone the repository:
```sh
git clone https://github.com/LavKalsi/CreditCardFraudDetector.git
cd CreditCardFraudDetector
```
2. Navigate to the `frontend` directory and install dependencies:
```sh
cd frontend
npm install
```
3. Start the React application:
```sh
npm start
```
### Backend Setup
1. Create and activate a virtual environment (optional but recommended):
```sh
python -m venv venv
source venv/bin/activate # On Windows, use `venv\Scripts\activate`
```
2. Install the required Python packages:
```sh
pip install -r res/requirements.txt
```
3. Run the backend server:
```sh
python res/FraudDetector.py
```
## Usage
1. Ensure both the frontend and backend servers are running.
2. Open your browser and navigate to `http://localhost:3000`.
3. Enter the transaction data.
4. Click the "Check" button to receive the fraud prediction.
## How It Works
The Credit Card Fraud Detector web app allows users to predict if a transaction is fraudulent. Here's how you can use it:
1. **Input Data:** Users can input transaction details into the provided fields on the web app.
2. **Submit for Prediction:** After entering the details, users click the "Check" button to submit the information for analysis.
3. **Backend Processing:** The frontend sends the transaction data to the backend Python server, where the machine learning model processes them.
4. **Receive Results:** The backend returns the prediction result (fraudulent or not) to the frontend, which is then displayed to the user.
## Backend Details
The backend is a Python Flask application that serves a machine learning model trained to predict fraudulent transactions. The backend files, including the model and Flask app, are located in the `res` folder.
### Files in `res` Folder
- `FraudDetector.py`: The Flask application that handles HTTP requests from the frontend.
- `creditcard.model`: The trained machine learning model.
- `requirements.txt`: The dependencies required for the Python backend.
## Contributing
Contributions are welcome! Please open an issue or submit a pull request if you have any improvements or suggestions.
1. Fork the repository.
2. Create your feature branch (`git checkout -b feature/your-feature`).
3. Commit your changes (`git commit -am 'Add your feature'`).
4. Push to the branch (`git push origin feature/your-feature`).
5. Open a pull request.
## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
## Contact
LavKalsi - [GitHub](https://github.com/LavKalsi)
Feel free to contact me if you have any questions or suggestions!