The Fake News Detection System is an AI-powered web application designed to instantly identify and combat misinformation online. Using advanced machine learning algorithms, the system analyzes news articles and determines whether the content is likely to be fake or genuine. This system is built with Flask and Streamlit, ensuring seamless performance and an easy-to-use interface.
This project is an initial step in my learning journey to understand and implement machine learning models for text classification. It aims to showcase how machine learning and web development technologies can be used together to build a practical application for real-world problems.
- Fake News Detection: Uses a machine learning model to classify articles as real or fake.
- Real-Time Results: Provides immediate feedback based on the content entered.
- User-Friendly Interface: Built with Flask and Streamlit for easy interaction.
- Model Training: The underlying model is trained on a dataset of labeled news articles to distinguish between authentic and fake news.
- Flask: A lightweight web framework for Python that is used for building the backend of the application.
- Streamlit: A fast and easy way to create beautiful data applications with minimal code, used for the frontend of the project.
- Python: The primary programming language used for both the backend and the machine learning model.
- Machine Learning: A text classification model that is trained using natural language processing techniques.
- scikit-learn: A Python library for machine learning that was used to build the model.
- Pandas & NumPy: Libraries for data manipulation and analysis.
- HTML/CSS: Used for creating the frontend structure of the application.
To run this project locally, follow the steps below.
Make sure you have the following installed:
- Python 3.x
- pip (Python package installer)
- Clone the repository:
git clone https://github.com/username/fake-news-detection.git