Fake news has been spreading in greater numbers and has generated more and more misinformation, one of the clearest examples being the United States presidential elections of 2016, for which a lot of false information was circulated before the votes that improved the image of Donald Trump overs Hilary’s Clinton (Singh et al. n.d.). Because fake news is too much, it becomes necessary to use computational tools to detect them; this is why the use of algorithms of Machine Learning like “CountVectorizer”, “TfidfVectorizer”, a Naive Bayes Model and natural language processing for the identification of false news in public data sets is proposed.