https://github.com/imharshag/fake-news-detection-using-ml

This project aims to tackle this problem by developing a system that can effectively detect fake news using machine learning techniques.
https://github.com/imharshag/fake-news-detection-using-ml

classification-algorithm confusion-matrix css decision-trees django html logistic-regression machine-learning randomforest regression-algorithms supervised-learning

Last synced: about 2 months ago
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This project aims to tackle this problem by developing a system that can effectively detect fake news using machine learning techniques.

• Host: GitHub
• URL: https://github.com/imharshag/fake-news-detection-using-ml
• Owner: imharshag
• Created: 2024-02-29T15:59:20.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2024-08-01T13:13:06.000Z (11 months ago)
• Last Synced: 2025-04-23T06:48:56.591Z (about 2 months ago)
• Topics: classification-algorithm, confusion-matrix, css, decision-trees, django, html, logistic-regression, machine-learning, randomforest, regression-algorithms, supervised-learning
• Language: Python
• Homepage:
• Size: 12.4 MB
• Stars: 8
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • Changelog: news-sample.csv

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README

        
# 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.

![Fake News Detection](https://harshag.vercel.app/_next/image?url=%2F_next%2Fstatic%2Fmedia%2FFake_News_Detection_using_Machine_Learning.fbe6865a.png&w=1080&q=75)

### 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](https://drive.google.com/file/d/1LQZDPBiEmd4aYmmc1j1u7ksAM-XfVGhB/view?usp=drive_link)

### Technologies Used

- HTML/CSS 🌐

- JavaScript ⚙️

- Python 🐍

- Django 🕸️

### Contact Information

For inquiries or feedback, please contact **[Harsha G](mailto:[email protected])**

### Contributing

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