ANALYSIS OF MACHINE LEARNING ALGORITHMS FOR EMAIL  SPAM DETECTION: A FOCUS ON NAIVE BAYES AND LOGISTIC  REGRESSION

Kondragunta Rama Krishnaiah, Harish H

ANALYSIS OF MACHINE LEARNING ALGORITHMS FOR EMAIL SPAM DETECTION: A FOCUS ON NAIVE BAYES AND LOGISTIC REGRESSION

Authors

Kondragunta Rama Krishnaiah, Harish H

Keywords:

Email Spam Detection, Machine Learning Algorithms, Logistic Regression, Naive Bayes and Text Classification

Abstract

Email spam, consisting of unsolicited and often harmful messages, continues to be a significantissue for users and organizations alike. To address this, machine learning algorithms have beenwidely explored for spam email detection

References

Suryawanshi, S., Goswami, A., Patil, P. (2019). Email Spam Detection: An Empirical Comparative Study of Different ML and Ensemble Classifiers. 69-74. 10.1109/IACC48062.2019.8971582.

Karim, A., Azam, S., Shanmugam, B., Krishnan, K., Alazab, M. (2019). A Comprehensive Survey for Intelligent Spam Email Detect ion. IEEE Access, 7, 168261-168295. https://doi.org/10.1109/ACCESS.2019.2954791

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Published

2023-08-05

How to Cite

Kondragunta Rama Krishnaiah, Harish H. (2023). ANALYSIS OF MACHINE LEARNING ALGORITHMS FOR EMAIL SPAM DETECTION: A FOCUS ON NAIVE BAYES AND LOGISTIC REGRESSION. Journal of Computational Analysis and Applications (JoCAAA), 31(1), 1261–1269. Retrieved from https://www.eudoxuspress.com/index.php/pub/article/view/2659