Fake News Detection System Project Description This project focuses on developing a Fake News Detection System using a combination of decision tree algorithms and natural language processing (NLP). The primary goal is to create a robust and accurate system capable of identifying misinformation in textual content. The solution is implemented in Python, utilizing Jupyter Notebooks for development and Streamlit for deployment. Motivation The rise of misinformation and fake news in the digital era poses a significant threat to public discourse and decision-making. This project aims to address this issue by providing a tool that can automatically identify and flag potentially false information. By leveraging machine learning techniques, the system contributes to the ongoing efforts to promote information integrity and combat the spread of misleading content. Tools and Technologies Programming Language: Python Machine Learning Algorithm: Decision Tree Natural Language Processing (NLP): Techniques include tokenization, stemming, and feature extraction. Development Environment: Jupyter Notebooks for interactive and transparent development. Web Application Framework: Streamlit for seamless deployment and user-friendly interface. Version Control: Git Collaboration: GitHub Features Decision Tree Algorithm: The core of the system utilizes a decision tree model, a powerful machine learning algorithm capable of making accurate predictions based on input features.

Natural Language Processing (NLP): NLP techniques are employed to preprocess and analyze textual data. This includes tasks such as tokenization, stemming, and feature extraction, enhancing the model's understanding of language nuances.

Python & Jupyter Notebooks: The project is developed using Python, with Jupyter Notebooks providing an interactive and exploratory environment for model development. This ensures transparency and ease of understanding for future contributors.

Streamlit Deployment: The system is deployed using Streamlit, offering a user-friendly interface for users to interact with the fake news detection model. Streamlit allows for seamless deployment and easy sharing of the application.