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Fraud Call Detection

This application is designed to detect fraudulent calls by analyzing audio recordings. Upload an audio file to see if it is classified as a fraud call or a normal call. This project was developed as part of the GSLab Hackathon 2024, showcasing our innovative approach to fraud detection using speech recognition and machine learning techniques.

Dashboard Screenshot

Table of Contents

Installation

Prerequisites

  • Python 3.8 or higher
  • Docker (optional, for containerized deployment)

Steps

  1. Clone the repository:

    git clone https://github.com/yourusername/fraud-call-detection.git
cd fraud-call-detection

  2. Set up a virtual environment:

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

  3. Install dependencies:

    pip install -r requirements.txt

  4. Download NLTK data:

    python -m nltk.downloader stopwords wordnet

Using Docker

  1. Build the Docker image:

    docker build -t fraud-call-detection .

  2. Run the Docker container:

    docker run -p 5000:5000 fraud-call-detection

Usage

  1. Start the Flask application:

    flask run --host=0.0.0.0

  2. Open your web browser and navigate to:

    http://localhost:5000

  3. Upload a call recording to determine whether it is fraud or not.

Technologies Used

  • Flask: A micro web framework for Python.
  • SpeechRecognition: Library for performing speech recognition.
  • NLTK: Natural Language Toolkit for text processing.
  • pandas: Data manipulation and analysis library.
  • scikit-learn: Machine learning library.
  • Docker: Containerization platform.