π°π« 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.
- HTML/CSS Usage: Utilized HTML/CSS for designing the user interface, ensuring an attractive and responsive layout. π¨
- Machine Learning Algorithms:
- Decision Tree: Implemented Decision Tree algorithm for classification of news articles. π³
- Random Forest: Utilized Random Forest for ensemble learning to improve the accuracy of fake news detection. π²
- Logistic Regression Analysis: Employed Logistic Regression for binary classification of news articles. π
- Text Analysis:
- WordCloud: Generated WordClouds to visualize the most frequent words in both fake and real news articles. βοΈ
- Word Count: Calculated word count in news articles for feature extraction and analysis. π’
- Evaluation Metrics:
- Confusion Matrix: Utilized Confusion Matrix to evaluate the performance of the machine learning models in classifying fake and real news articles. π
Project results and related documents
- HTML/CSS π
- JavaScript βοΈ
- Python π
- Django πΈοΈ
For inquiries or feedback, please contact Harsha G
π οΈ Contributions are welcome! Feel free to open an issue or submit a pull request with any improvements or bug fixes.