Comparative Study of Email Spam Filtration Using Machine Leaning Algorithms

Rahul Gupta, Akash Raghuwanshi

Abstract: In modern life, social network is an online platform extensively used as communication tool in order to build social relation, and email is one of them. Spam mails have become a serious matter of concern on internet in recent times. Hackers get the chance to abuse emails and steal its information for an illegal purpose. Classification of Emails presents a lot of challenges because of large number of mails. Different machine learning techniques such as K-Nearest Neighbour, Na?ve Bayes, SVM and Decision tree have repeatedly been used to tackle these spam mails. Our approach is based on using the KNN algorithm - one of the simplest and efficient classification algorithms and to obtain the maximum accuracy for the best results having small processing time enough for detecting spam mails. Feature extraction is implemented using Particle Swarm Optimization (PSO) which efficiently provides good result for the proposed algorithms in this paper.

Keywords: Spam detection, KNN, Na?ve Bayes, Feature selection, PSO

How to Cite?: Rahul Gupta, Akash Raghuwanshi, "Comparative Study of Email Spam Filtration Using Machine Leaning Algorithms", Volume 12 Issue 8, August 2023, International Journal of Science and Research (IJSR), Pages: 2344-2349, https://www.ijsr.net/getabstract.php?paperid=SR23827122535, DOI: https://dx.doi.org/10.21275/SR23827122535