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@article{154441,
        author = {Jhanvi Hemal Shah and Deekshita Bharat Nirmal and Manisha K.Ahirrao and Soham Jayant Prabhu and Prathamesh Mhalu Sanap},
        title = {Fake News Detector},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {8},
        number = {11},
        pages = {195-199},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=154441},
        abstract = {Fake News has become one of the biggest problems in the current society. Fraudulent stories are high the power to change ideas, facts and can be a very dangerous weapon to influence society. A proposed project strategy to detect 'false stories', that is, future misleading news from sources not reputable. By creating a Passive Aggressive Classifier model, false news can seen. The data science community has responded by taking action against the problem. Icon it is not possible to describe the stories as real or fake. The proposed project therefore uses data sets trained using a vectorizer calculation method to detect false stories and their accuracy tested using machine learning algorithms. Reduced the authenticity of the news ecosystem as is more widely distributed on social media than popular real-life stories. It is one of his major problems with the ability to change ideas and to influence decisions and disrupt them how people react to real stories.  },
        keywords = {fake news, passive aggressive, vectorizer calculated, confusion matrix },
        month = {},
        }