The dissemination of fake news in the digital age has become a pressing concern, leading to misinformation, polarization, and distrust in media sources. This project addresses this critical issue by developing a machine learning-based system for the detection of fake news articles. The purpose is to provide users with a reliable tool for discerning between genuine and deceptive content, thereby promoting informed decision-making and trust in digital information sources.

Beginning with the collection of a balanced dataset comprising labeled news articles categorized as real or fake, the project employs text preprocessing techniques to clean and tokenize the data. Feature extraction methods capture semantic and syntactic characteristics, enabling the classification of articles using various machine learning algorithms, including logistic regression, Naive Bayes, and decision trees.

The anticipated outcome is the creation of a robust model capable of accurately distinguishing between real and fake news articles. The findings of this project hold significant implications for combating misinformation and fostering integrity in digital information dissemination.