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- Publisher
- Springer Journals
- Copyright
- Copyright © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
- ISSN
- 2198-5804
- eISSN
- 2198-5812
- DOI
- 10.1007/s40745-023-00479-z
- Publisher site
- See Article on Publisher Site
Abstract
In this paper, the machine learning algorithm Naive Bayes Classifier is applied to the Kaggle spam mails dataset to classify the emails in our inbox as spam or ham. The dataset is made up of two main attributes: type and text. The target variable "Type" has two factors: ham and spam. The text variable contains the text messages that will be classified as spam or ham. The results are obtained by employing two different Laplace values. It is up to the decision maker to select error tolerance in ham and spam messages derived from two different Laplace values. Computing software R is used for data analysis.
Journal
Annals of Data Science – Springer Journals
Published: Jun 9, 2023
Keywords: Naïve Bayes Classifier; Machine learning; Predictive analytics; Artificial intelligence; Supervised machine learning; C38; C65