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
• Features
• Installation
• Usage
• How It Works
• Backend Details
• Contributing
• License
• 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:

   git clone https://github.com/LavKalsi/CreditCardFraudDetector.git
   cd CreditCardFraudDetector

2. Navigate to the frontend directory and install dependencies:

   cd frontend
   npm install

3. Start the React application:

   npm start

Backend Setup

1. Create and activate a virtual environment (optional but recommended):

   python -m venv venv
   source venv/bin/activate  # On Windows, use `venv\Scripts\activate`

2. Install the required Python packages:

   pip install -r res/requirements.txt

3. Run the backend server:

   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 file for details.

Contact

LavKalsi - GitHub

Feel free to contact me if you have any questions or suggestions!