Rapid and a vast increase in fake news, defined as probably incorrect information spread with the goal of fraud.Spread of this misinformation is a severe danger that can cause political polarisation. Fake news can make people their political parties or hate a particular community. Thus, fake news is a phenomenon that significantly impacts people's social lives.This system will use NLP techniques to detect fake and misleading news stories from non-reputable sources. We apply the term frequency-inverse document frequency (TF-IDF) of bi-grams and probabilistic context-free grammar (PCFG) detection to a corpus of about 11,000 articles. We find that TF-IDF of bi-grams fed into a Stochastic Gradient Descent model performs exceptionally well at identifying non-credible sources, with PCFGs having slight effects on recall. However, we are sceptical about the generalizability of these findings and include ample discussion on the next steps for exploration in this space.