Fake News Detector

Fake news has become a critical challenge in today’s digital era, where misinformation spreads rapidly through social media and online platforms. This project focuses on developing a Fake News Detection system using machine learning techniques to classify news articles as real or fake. The system was trained on labeled datasets, applying natural language processing (NLP) methods such as tokenization, stop-word removal, and feature extraction (TF-IDF/word embeddings). Various classification models were tested, including Logistic Regression, Naïve Bayes, and Random Forest, to evaluate performance and accuracy. The results demonstrated that machine learning-based approaches can effectively identify misinformation with high accuracy, providing a valuable tool for improving information credibility and reducing the spread of fake news.