A Review Article On Enhancing Email Spam Filter’s Accuracy  Using Machine Learning

Livingston Jeeva; Ijtaba Saleem Khan

doi:10.55524/ijircst.2023.11.4.2

Authors

Livingston Jeeva M.Tech. Scholar, Department of Computer Science and Engineering, Integral University, Lucknow, India Author
Ijtaba Saleem Khan Associate Professor, Department of Computer Science and Engineering, Integral University Lucknow, India Author

DOI:

https://doi.org/10.55524/ijircst.2023.11.4.2

Keywords:

Naïve Bayes, Email Spam, Filter, Accuracy, Classification, Machine Learning

Abstract

In today’s era, almost everyone is using emails on their daily basis. In our proposed research, we suggest a machine learning-based strategy for enhancing email spam filters' accuracy. Traditional rule-based filters have grown less effective as spam emails have multiplied exponentially. Models can be trained to identify emails as spam or not using machine learning algorithms, particularly supervised learning. We need to create a simple and straightforward machine learning model in order to reach more accurate results while categorizing email spam. We picked the Naive Bayes technique for our model since it is quicker and more accurate than other algorithms. The suggested method can have incorporated into current email systems to enhance spam filtering functionality. This review paper provides an overview of the machine learning model we have suggested.

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