Naïve Bayes Classifier Model for Detecting Spam Mails

Shrawan Kumar (), Kavita Gupta () and Manya Gupta ()
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Shrawan Kumar: University of Delhi
Kavita Gupta: University of Delhi
Manya Gupta: University of Delhi

Annals of Data Science, 2024, vol. 11, issue 6, No 1, 1887-1897

Abstract: 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.

Keywords: Naïve Bayes Classifier; Machine learning; Predictive analytics; Artificial intelligence; Supervised machine learning (search for similar items in EconPapers)
JEL-codes: C38 C65 (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s40745-023-00479-z

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