DETECTING AND CLASSIFYING FRAUDULENT SMS AND EMAIL WITH A ROBUST MACHINE LEARNING APPROACH
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Abstract
Spam is an unwanted message or SMS sent onmobile phones whose content may bemalicious. Scammers sendfake text messages to trick people into responding to their SMSand they may hack personal information, password, accountnumber, etc. To avoid being tricked by scammers, proposed amodel based on Machine learning Algorithms. The proposedmodel is implemented using the Naïve Bayes algorithm and termfrequency-inverse document frequency vectorizer. Obtainedthe dataset from Kaggle and trained the model using it. Thismodel consists of a local host website which is obtained throughPyCharm IDE. Obtained results show that the model accuracyof 95% and a precision of 100%
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