🌟 Fake News Detection 🌟

📝 Description

A Python project that implements a machine learning model using TensorFlow, Keras, and other libraries to classify news articles as fake or real.

🚀 Tech Stack

📂 Project Structure

.
├── fake_news_detection.py   # Main script for fake news detection
├── True.csv                 # Dataset of true news (example)
├── Fake.csv                 # Dataset of fake news (example)
├── README_template.md.jinja # The Jinja template for the README
├── render_readme.py         # Script to render the README
├── README.md                # The generated README file
└── requirements.txt         # Project dependencies (create this if it doesn't exist)

⚙️ Installation

To install dependencies using pip:

pip install -r requirements.txt

🔐 Environment Variables

To run this project, you may need to set the following environment variables. Create a .env file in the project root and add them there:

# No specific environment variables listed for this project

💻 Usage

To run the main script:

python main.py

🚀 Deployment

This section outlines how to deploy the {{ project_name }} project.

Deployment steps can vary greatly depending on your target environment (e.g., a web server, a cloud platform like AWS or Heroku, a Docker container).

Here is a general outline of steps you might need to consider and fill in:

1. Prerequisites: List any software, accounts, or tools required for deployment (e.g., Docker, a cloud provider CLI, a specific server OS).
2. Build (if applicable): If your project requires building (e.g., packaging a model, creating a Docker image), provide the necessary commands.

   # Example build command
   # docker build -t fake-news-detector .

3. Configuration: Explain how to configure the project for the deployment environment, especially regarding environment variables or configuration files.
4. Deployment Steps: Provide the specific commands or procedures to get the application running in the target environment.

   # Example deployment command
   # ssh your_server 'cd /path/to/app && git pull origin main && pip install -r requirements.txt && python fake_news_detection.py'
   # or
   # heroku create your-app-name
   # git push heroku main

5. Running/Starting the Application: Explain how to start the application process in the deployed environment, especially if it's a background service.
6. Verification: Describe how to check that the deployment was successful and the application is running as expected.

(Please replace this general outline with the specific, detailed steps for deploying YOUR project.)

🤝 Contributing

We welcome contributions to this project!

To contribute:

1. Fork the repository 🍴
2. Create your feature branch: git checkout -b feature/YourFeature
3. Commit your changes: git commit -m 'Add YourFeature'
4. Push to the branch: git push origin feature/YourFeature
5. Open a pull request 📬

Please follow the coding guidelines and check the Makefile or CONTRIBUTING.md file if available for more details.

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.