With technological advancements and increment in Mobile Phones supported content advertisement, because the use of SMS phones has increased to a big level to prompted Spam SMS unsolicited Messages to users, on the complexity of reports the quality of SMS Spam is expanding step by step. These spam messages can lead loss of personal data as well. SMS spam detection which is relatively equal to a replacement area and systematic literature review on this area is insufficient. SMS detection are often dealed using various machine learning techniques which as a feature called SMS spam filtering which separates spam or ham . This Paper aims to match treats spam detection as a basic two class document classification problem. The Classification will comprise of classification algorithm with extractions and different dataset collected which uses a classification feature to filter the messages . In this web journal, we are going center on creating a Naïve Bayes show for spam message identification, and utilize flash as it could be a web benefit advancement micro framework in python to form an API for show. The Comparison has performed using machine learning and different algorithm techniques.
SMS Spam, Detection, Machine Learning Techniques, Content Features
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