Readme drafted with gpt-4o

ML Model Deployment on AWS

This project is an educational initiative aimed at deploying machine learning models detecting fake news on AWS. It demonstrates the process of building, saving, and deploying a simple text classification model using Flask and Scikit-learn.

Project Structure

• application.py: The main Flask application that serves the model and handles prediction requests.
• basic_classifier.pkl: A pre-trained Naive Bayes classifier model.
• count_vectorizer.pkl: A pre-trained CountVectorizer for text feature extraction.

Prerequisites

• Python 3.x
• Flask
• Scikit-learn
• AWS account for deployment

Setup

1. Clone the repository:

   git clone <repository-url>
   cd <repository-directory>

2. Create 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. Run the application locally:

   python application.py

   The application will be available at http://localhost:5000.

Usage

• Send a POST request to /predict with a JSON payload containing the text to classify:

  {
    "text": "Your text here"
  }

• The application will return a JSON response with the prediction.

License

This project is licensed under the MIT License

Acknowledgments

• Flask for providing a simple and powerful web framework.
• Scikit-learn for the machine learning tools.
• AWS for cloud infrastructure.