Fake News Detection

This is a web-based application that detects whether a given news article is real or fake. The application uses machine learning and natural language processing (NLP) techniques to classify the news as "Fake" or "Genuine".

Table of Contents

• Overview
• Features
• Technologies Used
• Project Setup
• How to Use
• Screenshots

Overview

Fake news is a serious problem in today’s digital age, leading to the spread of misinformation. This project aims to help mitigate this problem by using a machine learning model to identify fake news articles based on their content.

Features

• User-friendly Interface: Enter news content and receive an instant result.
• Real-time Prediction: Utilizes a trained model to classify news as either Fake or Genuine.
• Input Cleaning: Text preprocessing to remove URLs, HTML tags, digits, and punctuation for accurate predictions.

Technologies Used

• Backend: Flask (Python)
• Machine Learning: scikit-learn, Pandas, TfidfVectorizer, Decision Tree Classifier Model
• Frontend: HTML, CSS, Jinja2
• Data Storage: Vectorizer and Model saved using Pickle

Project Setup

Prerequisites

• Python 3.7 or later
• pip (Python package installer)

Installation

1. Clone the repository:

   git clone https://github.com/your-username/fake-news-detection.git
   cd fake-news-detection

2. Create a virtual environment:

   python -m venv venv

3. Activate the virtual environment:

   • On Windows:

     .\venv\Scripts\activate

   • On macOS/Linux:

     source venv/bin/activate

4. Install required packages:

   pip install -r requirements.txt

5. Run the Flask app:

   python app.py

6. Open your web browser and go to http://127.0.0.1:5000/.

How to Use

1. Open the web application.
2. Enter the news article text into the input box.
3. Click the "Check Now" button.
4. The application will return the result as either "It is a fake news" or "It is a genuine news".

Screenshots

Future Improvements

• Model Improvements: Implement additional machine learning models to improve accuracy.
• More Preprocessing: Enhance text preprocessing techniques.
• Data Updates: Keep the model updated with new datasets to stay current with evolving news trends.

Acknowledgements

• Thanks to the open-source community for providing valuable datasets and tools.
• scikit-learn, Flask, and Jinja2 are used extensively in this project.