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- W3201250517 abstract "Email users are increasing at a high rate and a huge number of people’s privacy is getting risked by spam email and it also kills valuable time of people most often. Spam email can be malicious as well as it can be of commercial use as in for marketing which are not desirable to us. Hence, detecting and filtering spam emails from several emails is a must work to do. There are enormous machine learning (ML) algorithms and some of them can be used to detect and analyze spam and unwanted emails. In this paper, we use the supervised ML technique on an existing email classification dataset where we explore Naive Bayes, Support Vector Machine, Random Forest Classifier. Along with observing the accuracy from these algorithms, we showed other performance metric like precision, recall and F1 score etc. We got a high rate of accuracy in each algorithm such as we got 98.8%, 97.6%, 91.5%, 97.8%, 98.5% accuracy in Multinomial Naive Bayes, Bernoulli Naive Bayes, Gaussian Naive Bayes, Random forest classifier, Support vector machine (SVM) respectively." @default.
- W3201250517 created "2021-09-27" @default.
- W3201250517 creator A5004225679 @default.
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- W3201250517 date "2021-07-08" @default.
- W3201250517 modified "2023-09-26" @default.
- W3201250517 title "An Analysis of Supervised Machine Learning Algorithms for Spam Email Detection" @default.
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- W3201250517 doi "https://doi.org/10.1109/acmi53878.2021.9528108" @default.
- W3201250517 hasPublicationYear "2021" @default.
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