• Akashdeep Ghosh
• Priyanka Gupta
• Arijit Paul
• Debayan Dey
• Abhijit Ghosh

The fake information on social sites and diverse different media is spreading and it is a matter of significant difficulty because of its capacity to cause a whole lot of social and countrywide harm with negative impacts. A lot of studies are already targeted at detecting it. This paper makes an evaluation of the studies associated with fake information detection. It explores the conventional system of studying fashions to select the best, as a way to create a version of a product with a supervised machine learning algorithm, which could classify fake information as real or false, with the aid of using the use of equipment like python scikit-learn, stemming for textual evaluation. This technique will bring about feature extraction and vectorization; we suggest the use of scikit-learn library to execute tokenization and feature extraction of textual content data, due to the fact this library includes beneficial equipment like Count Vectorizer and Tiff Vectorizer. Then, we can carry out feature selection methods, to test and choose the perfect fit features to acquire the best precision, in keeping with confusion matrix results.

Fake Information, Machine Learning, Count Vectorizer.

"FAKE NEWS DETECTION USING MACHINE LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 5, page no.i550-i553, May-2022, Available :http://www.jetir.org/papers/JETIR2205978.pdf

"FAKE NEWS DETECTION USING MACHINE LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 5, page no. ppi550-i553, May-2022, Available at : http://www.jetir.org/papers/JETIR2205978.pdf