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Title of the Project        :   A Cascaded Ensemble Based Youtube Spam Comments Detection using Machine Learning

Name of the Students  :     Chegireddy Keerthana, Shalini B, Swathi S

Register Number(s):          211419104046, 211419104244, 211419104282

 Name of the Guide:            Dr. A. Hemlathadhevi, M.E., Ph.d.,

ABSTRACT

YouTube possesses a spam filtering mechanism, although it consistently fails to deal with spam effectively. As a corollary, related studies on classifying YouTube spam comments have been done and classification experiments were performed. The subscribers of this social network might easily be impacted by these YouTube spam messages. Spam comments on YouTube have turned out to be a social issue and may spread more rapidly than the actual information. YouTube has had problems reducing the amount of malicious text posted by people trying to promote their videos or spread malicious links to steal their personal information. Many machine learning approaches are utilized to analyze enormous, complicated data, assisting professionals in predicting YouTube spam comments. The most accurate model is applied to forecast the spam YouTube comments is known as cascade ensemble model. The algorithms that are used in this model are Logistic regression, Random Forest, Multinomial Naive Bayes algorithms.