Fake News Detector

A React web application that uses a Python machine learning model to detect fake news. The backend Python files are deployed in the res folder.

Table of Contents

• Introduction
• Features
• Installation
• Usage
• How It Works
• Backend Details
• Contributing
• License
• Contact

Introduction

Fake News Detector is a web application built with React for the frontend and a Python machine learning model for the backend. It allows users to input news articles or headlines and receive a classification on whether the news is real or fake.

Features

• User-friendly Interface: Easy-to-use interface for checking the authenticity of news articles.
• Real-time Analysis: Quickly processes input to determine the likelihood of news being fake.
• Machine Learning: Utilizes a trained machine learning model for accurate predictions.

Installation

Prerequisites

• Node.js
• Python 3.x
• pip (Python package installer)

Frontend Setup

1. Clone the repository:

   git clone https://github.com/LavKalsi/FakeNewsDetector.git
   cd FakeNewsDetector

2. Navigate to the frontend directory and install dependencies:

   cd frontend
   npm install

3. Start the React application:

   npm start

Backend Setup

1. Create and activate a virtual environment (optional but recommended):

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

2. Install the required Python packages:

   pip install -r res/requirements.txt

3. Run the backend server:

   python res/Server.py

Usage

1. Ensure both the frontend and backend servers are running.
2. Open your browser and navigate to http://localhost:3000.
3. Enter the news article or headline you want to verify.
4. Click the "Check" button to receive the classification result.

How It Works

The Fake News Detector web app allows users to easily check the authenticity of news articles. Here's how you can use it:

1. Input News: Users can input a news article or headline into the provided text box on the web app.
2. Submit for Analysis: After entering the news, users click the "Check" button to submit the text for analysis.
3. Backend Processing: The frontend sends the news text to the backend Python server, where the machine learning model processes it.
4. Receive Results: The backend returns the analysis result (real or fake) to the frontend, which is then displayed to the user.

Backend Details

The backend is a Python Flask application that serves a machine learning model trained to classify news as real or fake. The backend files, including the model and Flask app, are located in the res folder.

Files in res Folder

• Server.py: The Flask application that handles HTTP requests from the frontend.
• DecisionTree.model,LogisticRegression.model,RandomForest.model: The trained machine learning models.
• requirements.txt: The dependencies required for the Python backend.

Contributing

Contributions are welcome! Please open an issue or submit a pull request if you have any improvements or suggestions.

1. Fork the repository.
2. Create your feature branch (git checkout -b feature/your-feature).
3. Commit your changes (git commit -am 'Add your feature').
4. Push to the branch (git push origin feature/your-feature).
5. Open a pull request.

License

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

Contact

LavKalsi - GitHub

Feel free to contact me if you have any questions or suggestions!