Welcome to the Fake News Detector project! This project was created as part of my college coursework, and I'm excited to share it with the developer community. If you are interested in contributing to this project or using it as a basis for your own development, please read on.

FND is a web-based application designed to detect fake news articles. It uses machine learning models to analyze news articles and predict whether they are real or fake. The goal of this project is to provide a tool that can help users identify unreliable news sources and combat the spread of misinformation.

• Live News Monitoring: View real-time predictions for news articles.

• News Quiz: Test your fake news detection skills by taking our news quiz.

• Check News by Title: Enter a news title to see if it's predicted as real or fake.

• User Collaboration: I encourage other developers to collaborate and improve this project further.

To get started with this project, follow these steps:

1. Cloning the repository

git clone https://github.com/DJDarkCyber/Fake-News-Detector

2. Install the required libraries for python

cd Fake-News-Detector/app/FakeNewsDetectorAPI/ && pip install -r requirements.txt

3. Install the required libraries for js

cd ../fake-news-detector-frontend && npm install

4. Deployment

Open terminal and cd to project root folder and run

cd app/FakeNewsDetectorAPI/ && python manage.py migrate && python manage.py runserver

Open another terminal and cd to project root folder and run

cd app/fake-news-detector-frontend/ && npm start

All set if everything running without errors. Now the deployed web application should open in a browser. If not, open a browser and navigate to http://localhost:3000

I welcome contributions from fellow developers. If you have ideas for new features, improvements, or bug fixes, please open an issue or submit a pull request. Your contributions will be greatly appreciated and will help make this project even better.

• Enhanced Machine Learning Models: Improve the accuracy of the fake news detection models.
• User Profiles: Allow users to create profiles and track their quiz scores.

If you have any questions or suggestions, feel free to reach out to me at dark_agent_437@protonmail.com.

If you have any issues, raise issue.

Thank you for considering contributing to the Fake News Detector project. Together, we can make a positive impact on the fight against misinformation.

Happy coding!