Fake news refers to misinformation, disinformation or mal-information which is spread through word of mouth and traditional media and more recently through digital forms of communication such as edited videos, memes, unverified advertisements and social media propagated rumours

This dataset consists of about 40000 articles consisting of fake as well as real news. Our aim is train our model so that it can correctly predict whether a given piece of news is real or fake.The fake and real news data is given in two separate datasets with each dataset consisting around 20000 articles each.

There is also a version of it available on Kaggle. https://www.kaggle.com/clmentbisaillon/fake-and-real-news-dataset

Trained a TF-IDF(Bidirectional) Naive Bayes model with competitive performance and with accuracy of 91%