SPAM DETECTION USING ARTIFICIAL INTELLIGENCE

Abstract

The naive Bayes classifier, a key supervised learning algorithm rooted in Bayes Theorem, is notable for its speed, accuracy, and reliability, especially with large datasets. It operates on the assumption of class conditional independence, treating features within a class as unrelated to each other. This means it doesn't consider potential connections between characteristics like age, region, income, and loan history when assessing a loan applicant's suitability. Despite possible correlations, the classifier simplifies calculations by treating these traits as distinct, hence earning its "naive" label.