Fake News Detection Using Machine Learning

Overview

📰🚫 Fake news is a significant issue in today's digital landscape. This project aims to tackle this problem by developing a system that can effectively detect fake news using machine learning techniques.

Key Features

• HTML/CSS Usage: Utilized HTML/CSS for designing the user interface, ensuring an attractive and responsive layout. 🎨
• Machine Learning Algorithms:
  • Decision Tree: Implemented Decision Tree algorithm for classification of news articles. 🌳
  • Random Forest: Utilized Random Forest for ensemble learning to improve the accuracy of fake news detection. 🌲
  • Logistic Regression Analysis: Employed Logistic Regression for binary classification of news articles. 📈
• Text Analysis:
  • WordCloud: Generated WordClouds to visualize the most frequent words in both fake and real news articles. ☁️
  • Word Count: Calculated word count in news articles for feature extraction and analysis. 🔢
• Evaluation Metrics:
  • Confusion Matrix: Utilized Confusion Matrix to evaluate the performance of the machine learning models in classifying fake and real news articles. 📊

Document 📄

Project results and related documents

Technologies Used

• HTML/CSS 🌐
• JavaScript ⚙️
• Python 🐍
• Django 🕸️

Contact Information

For inquiries or feedback, please contact Harsha G

Contributing

🛠️ Contributions are welcome! Feel free to open an issue or submit a pull request with any improvements or bug fixes.