• Miss. Pratiksha Mantri
• Dr. Ranjit keole

The increasing volume of advice bulk e-mail (also known as spam) has generated a need for reliable anti-spam filters. Machine learning techniques now days used to directly filter the spam e-mail in a very good rate. In this paper we review some of the most desired machine learning methods (Bayesian classification, k-NN, ANNs, SVMs, Artificial immune system and rough sets) and of their bearing to the problem of spam Email classification. Electronic mail has deleted communication methods for many organizations as well as individuals. This method is used for fraudulent gain by spammers through sending unsolicited emails. This article aims to describe a method for detection of spam emails with machine learning algorithms that are optimized with bio-inspired methods. A literature review is carried to figurative the efficient methods applied on different datasets to achieve good results. This research was done to implement machine learning models using Naïve Bayes, Support Vector Machine, Random Forest, Decision Tree and Multi-Layer Perceptron on seven different email datasets, along with feature extraction and pre-processing. The bio-inspired algorithms like Particle Swarm Optimization and Genetic Algorithm were implemented to evaluate the performance of classifiers. Multinomial Naïve Bayes with Genetic Algorithm performed the good overall. The comparison of our results with other machine learning and bio-inspired models to show the good suitable model is also discussed.

Machine Learning, Spam Email, Bio-Inspired, Spammers, Naïve Bayes Classifier Method, K-Nearest Neighbor Method, Cross-Validation.

"A Review on Spam Email Detection using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 4, page no.a252-a255, April-2022, Available :http://www.jetir.org/papers/JETIR2204033.pdf

"A Review on Spam Email Detection using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 4, page no. ppa252-a255, April-2022, Available at : http://www.jetir.org/papers/JETIR2204033.pdf