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– Abstract
Nowadays communication plays a major role in everything be it professional
or personal. Email communication service is being used extensively because of its
free use services, low-cost operations, accessibility, and popularity. Emails have one
major security flaw that is anyone can send an email to anyone just by getting their
unique user id. This security flaw is being exploited by some businesses and
ill-motivated persons for advertising, phishing, malicious purposes, and finally fraud.
This produces a kind of email category called SPAM.
Spam refers to any email that contains an advertisement, unrelated and
frequent emails. These emails are increasing day by day in numbers. Studies show
that around 55 percent of all emails are some kind of spam. A lot of effort is being
put into this by service providers. Spam is evolving by changing the obvious markers
of detection. Moreover, the spam detection of service providers can never be
aggressive with classification because it may cause potential information loss to
incase of a misclassification.
To tackle this problem we present a new and efficient method to detect spam
using machine learning and natural language processing. A tool that can detect and
classify spam. In addition to that, it also provides information regarding the text
provided in a quick view format for user convenience.

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