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Fake News Detection System using Python Machine Learning
The Fake News Detection System is an innovative machine learning project designed to classify online news articles as either real or fake. In today’s digital world, misinformation spreads quickly across social media and online platforms. This project provides a practical solution by applying Natural Language Processing (NLP) techniques and AI algorithms to verify the authenticity of news content. Developed as a Python Django web application with a MySQL database, this project is ideal for final year students looking for hands-on experience in AI, Machine Learning, and Data Science.
About the Project
The Fake News Detection System uses text preprocessing techniques such as stopword removal, lemmatization, and punctuation handling. The processed text is then transformed into numerical features using TF-IDF vectorization and combined with additional parameters like text length, punctuation percentage, and capitalization ratio. A Logistic Regression model trained on labeled datasets of fake and real news provides accurate predictions. Evaluation metrics such as accuracy score and classification report validate the model’s performance. By using sparse matrix operations, the system efficiently handles large text datasets without consuming excess memory.
Web Application Features
The complete system has been deployed as a Django 5 web application with user-friendly interfaces. Key modules include:
- Home Page: Static page with project introduction and highlights.
- About Page: Explains the working of the Fake News Prediction System.
- Login Page: Secure admin login with validation from the MySQL database.
- Contact Us: Static page for reaching out to the project developer.
- System Information: Displays the libraries, frameworks, and modules used in the project.
- Fake News Detection Module: Interactive form where users can input any news content to check its authenticity.
- Detection History: Stores and displays past news checks for easy tracking.
- Dataset Information: Provides details about the dataset used for training and testing the model.
Why Choose This Project?
This project is an excellent choice for B.Tech, M.Tech, MCA, and Computer Science final year students. It covers important concepts of Artificial Intelligence, Machine Learning, and Web Development. Students will gain practical skills in Python programming, Django framework, MySQL database integration, Natural Language Processing, and model deployment. The Fake News Detection System not only serves as a valuable academic project but also addresses a highly relevant real-world problem: the spread of fake news.
Conclusion
With its combination of AI-powered news verification and a modern web application, this Fake News Detection System is a complete end-to-end project. It can be deployed on local servers or cloud platforms, extended with APIs, or integrated with social media monitoring tools. For students looking for a final year machine learning project that is both practical and impactful, this is a perfect choice.
