An Efficient Email Spam Detection using Support Vector Machine
K sai Prasanthi1, T Deepika2, S Anudeep3, M Sai Koushik4
1K Sai Prasanthi, Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, AP, India.
2T Deepika, Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, AP, India.
3S Anudeep, Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, AP, India.
4M Sai Koushik, Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, AP, India.
Manuscript received on November 13, 2019. | Revised Manuscript received on 20 November, 2019. | Manuscript published on December 10, 2019. | PP: 5258-5262 | Volume-9 Issue-2, December 2019. | Retrieval Number: B9001129219/2019©BEIESP | DOI: 10.35940/ijitee.B9001.129219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: This research paper proposes the electronics mail is small known as E-mail is used for communication between the people to person. E mail is providing as an necessary contribution for messaging by internet. Spam e mails are the unwanted messages that arise in high volume and are used by spammers for revealing users personal credentials. These e mails are regularly some sort of company/control announcement or viruses that the user receive without any notification. So as to defeat it, there need aid exactly existing frameworks that still don’t keep them from striking. Therefore, there is a require should manufacture and proficient framework that adequately detects and more keeps the spam messages In those server utilizing the Naïve bayes classifier. Naïve bayes classifier is a mainstream statistical classifier utilized fundamentally for content arrangement.
Keywords: Ham, SVM Classifier, Naïve Bayes Classifier, Email, Spam
Scope of the Article: Classification