Fake News Detection

A Step-by-Step Guide : - Fake news has become a significant problem in today's digital world, where misinformation spreads rapidly. In this project, we built a Fake News Detection System using Machine Learning (ML) and Natural Language Processing (NLP) to classify news articles as Fake News or Real News.

This project uses Python, scikit-learn, and NLP techniques to: 

* Process and clean news data

* Convert text into numerical features using TF-IDF (Term Frequency-Inverse Document Frequency)

* Train a Logistic Regression model to classify Fake vs. Real news

*  Provide a user-friendly web interface (Streamlit)

* Announce results via Text-to-Speech (TTS) for better accessibility

Project Components

1.Dataset (fake_news_dataset.csv) → Contains real and fake news headlines

2.Text Preprocessing → Removing stopwords and special characters

3.F-IDF Transformation → Converting text into numerical data

4.Machine Learning Model → Logistic Regression for classification

5.Web App (Streamlit) → Allows users to check news credibility

6.Dropdown News Selection → Users can select pre-defined news headlines

7.Text-to-Speech (TTS) Support → Announces the result aloud

Github