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eBay Auction Fraud Detector

GitHub

Overview

Welcome to the eBay Auction Fraud Detector, a Python-based tool that utilizes machine learning to detect shill bidding in eBay auction item pages. Shill bidding refers to the practice of artificially inflating bids on an auction to deceive genuine bidders. This program aims to promote fairness and transparency in online auctions by identifying potential instances of shill bidding.

Key Features

  • Machine Learning Algorithms: The program employs advanced machine learning algorithms to analyze bidding patterns, user behaviors, and other relevant factors to detect shill bidding.
  • Auction Item Page Analysis: It focuses on analyzing eBay auction item pages, extracting bidding data, and generating insights for shill bidding detection.
  • Transparency and Fairness: By identifying and flagging potential shill bidding activities, the program helps create a more level playing field for genuine bidders.
  • Customization and Extensibility: The program provides flexibility for customization and further development to cater to specific auction platforms or unique requirements.

Installation

To run the eBay Auction Fraud Detector, follow these steps:

  1. Clone the repository:

    git clone https://github.com/Divuzki/ebay-auction-fraud-detector.git

  2. Navigate to the project directory:

    cd ebay-auction-fraud-detector

  3. Create a new virtual environment:

    • For macOS/Linux: 
      python3 -m venv env
source env/bin/activate
    • For Windows: 
      python -m venv env
.\env\Scripts\activate

    Activating the virtual environment is optional but recommended.

  4. Install the required dependencies:

    pip install -r requirements.txt

  5. Customize the program if needed by modifying the configuration files.

  6. Start the Django server:

    python manage.py runserver

  7. Access the program through the provided URL, e.g., http://localhost:8000/item/<str:item_id>/scan.

Usage

  1. Provide the URL of the eBay auction item page to the program.

  2. The program will analyze the bidding data and apply machine learning techniques to detect potential shill bidding.

  3. Results and insights will be displayed, indicating the likelihood of shill bidding activities.

  4. Use the program's output to investigate further and take appropriate action based on the identified patterns.

Training the Model

To train the machine learning model used for shill bidding detection, you can run the train_model.py script provided in the repository. Follow these steps:

  1. Ensure that you have the necessary dataset for training.

  2. Execute the script:

    python manage.py train_model

  3. The script will train the model based on the dataset and save the trained model for future use.

License

This project is licensed under the MIT License license. See the LICENSE file for details.

Author

Contact

For any inquiries, questions, or collaboration opportunities, please reach out to [email protected].

Thank you for your interest in the eBay Auction Fraud Detector. Together, let's foster fairness and transparency in online auctions by combating shill bidding practices. Happy detecting!