The advent of the World Wide Web and the rapid adoption of social media platforms (such as Facebook and Twitter) paved the way for information dissemination that has never been witnessed in human history before. With the current usage of social media platforms, consumers are creating and sharing more information than ever before, some of which are misleading with no relevance to reality.

The term fake news has become a buzzword these days. There was a time when if anyone needed any news, he or she would wait for the next-day newspaper. However, with the growth of online newspapers which update news almost instantly, people have found a better and faster way to be informed of the matter of his/her interest. Nowadays social-networking systems, online news portals, and other online media have become the main sources of news through which interesting and breaking news are shared at a rapid pace. However, many news portals serve special interest by feeding distorted, partially correct, and sometimes imaginary news that is likely to attract the attention of a target group of people.

Fake news has become a major concern for being destructive, sometimes spreading confusion and deliberate disinformation among the people. This project aims to use Natural Language Processing and Machine learning to detect Fake news based on the text content of the Article. After building the suitable Machine learning model to detect the fake/true news then deploy it into a web interface using python_Flask. Our study explores different textual properties that can be used to distinguish fake content from real. By using those properties, we train a combination of different machine learning algorithms using various ensemble methods and evaluate their performance on 5 real-world datasets.