Abstract
Abstract: Spam is an unsolicited text message or SMS that may contain malicious content and is sent on mobile devices. Fraudsters send fictitious texts in an attempt to get victims to reply to their messages, and they may also steal account numbers, passwords, and other private information. It was suggested to use a model built around algorithms for machine learning to prevent falling for scammers' tricks. The Naïve Bayes method and term frequencies-inverse document frequency vectorizer are used to implement the suggested model. acquired the dataset from the Kaggle database and used it to train the model. The PyCharm IDE can be used to access the local host webpage that makes up this model. The obtained findings indicate a 95% accuracy and 100% precision for the model.
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