Fake news, alternative facts are associated to each other since the time news was transmitted using news papers or radio. The fake news most recently came to light when the US Presidential election was underway. There have been several hoax stories where citizens, governments as well all other social elements are all affected by these stories. Facebook has been the amidst the controversy by the media houses for targeting the audiences and showing them posts to their support. This paper focus on detecting fake news with the help of various python libraries in association with counting features using ngrams and tf-idf. The system will be taking input from the user and then compare them with an existing data-set. We have compared various algorithms to find out the best working model that will fit our project and give a proper prediction for fake news.
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