A Machine Learning Based Approach for Improved Fake News Detection

Atul Suryawanshi, Vijendra Palash, Priyank Nayak


The News is significant piece of our life. In everyday life current news are useful to improve information what occur all throughout the planet. So the vast majority of people groups lean toward watching news a large portion of the people groups for the most part favor perusing paper promptly toward the beginning of the day appreciating with cup of tea. On the off chance that news is phony that will delude people groups now and then phony word used to get out bits of gossip about things or it will influence some political pioneer positions on account of phony news. So it's vital to track down the phony news. This exploration proposed an advanced framework to distinguish counterfeit news, yet now daily's information on web or online media is expanding immensely and it is so rushed to recognize news is phony or not by looking all information and it is tedious so we use characterization strategies to order colossal information. This paper proposed fake news detection system based on the classification approach such as Naïve bayes (NB), Support vector machine (SVM), K Nearest Neighbor (KNN) and Decision Tree (TD)


Machine Learning, Fake News, KNN, DT, NB, SVM, Python.

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DOI: https://doi.org/10.26483/ijarcs.v12i4.6756


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