https://github.com/pramod858/project-fraud-detection

https://github.com/pramod858/project-fraud-detection

Last synced: about 1 month ago
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• Host: GitHub
• URL: https://github.com/pramod858/project-fraud-detection
• Owner: Pramod858
• Created: 2023-05-26T14:48:32.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2024-02-16T13:31:48.000Z (10 months ago)
• Last Synced: 2024-02-16T14:39:03.260Z (10 months ago)
• Language: Jupyter Notebook
• Size: 46 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Credit Card Fraud Detection Project

## For Dataset [Click here](https://www.kaggle.com/datasets/mlg-ulb/creditcardfraud)

## Project needs `python > 3.9` version to run

![Screenshot 2023-11-16 183703](https://github.com/Pramod858/Project-Fraud-Detection/assets/80105491/becc17e9-60b1-4fb7-a905-278d50afef1a)

This project implements a Credit Card Fraud Detection system using machine learning. It includes a Flask web application with a Swagger API documentation, and the model is containerized using Docker for easy deployment.

## Overview

The goal of this project is to detect fraudulent credit card transactions through a machine learning model. The web application allows users to input transaction features, and the system predicts whether the transaction is fraudulent or not.

## Features

- Machine Learning Model: Trained model for predicting fraud based on transaction features.

- Flask Web Application: Provides a user-friendly interface for making predictions.

- Swagger API Documentation: Allows developers to understand and interact with the API endpoints.

- Docker Containerization: Simplifies deployment and ensures consistency across different environments.

## Usage

Follow these steps to build and run the project in a Docker container:

1. **Build Docker Image:**

    ```bash

    docker build -t  -f Dockerfile .

    ```

2. **Run Docker Container:**

    ```bash

    docker run -p 5000:5000 --name  

    ```

3. **Access the Application:**

    Open your web browser and navigate to [http://localhost:5000](http://localhost:5000).

4. **Stop the Container:**

    ```bash

    docker stop 

    ```

5. **Remove the Container:**

    ```bash

    docker rm 

    ```

6. **Remove the Image:**

    ```bash

    docker rmi 

    ```

7. **Clean Resources:**

    ```bash

    docker system prune

    ```

## Web Interface

The web interface provides input fields for transaction features (V1 to V28 and Amount). Enter the values and click the "Predict" button to see the prediction result.

## API Documentation

For detailed API documentation, visit [Swagger API Documentation](http://localhost:5000/apidocs).