• Indravadan Patel

Nowadays, a lot of spam emails are sent out every day, which causes email inboxes to be overflowing with inappropriate and undesired communications. Thus, managing a user's email has grown to be a significant effort. People can save time and irritation by identifying and eliminating spam from their inboxes. Several machine learning algorithms can identify these spam emails because they frequently share a lot of similarities. The Support Vector Machine (SVM) and its comparison with the Naïve-Bayes algorithm will be discussed in this study. Support Vector Machines (SVMs) and Naïve-Bayes Classifiers are used by some of the most efficient spam filters now in use to identify spam. SVM-based classifiers use email properties that are usually different between spam and non-spam. Support vectors are created from training emails that have been categorized by an expert. Future emails are classified as spam or non-spam using the generated SVM. Naïve-Bayes examines the frequency of specific terms in emails that are spam and those that are not. Based on these frequencies, it then calculates the likelihood that an email is spam. The simulation findings in this research show that the SVM approach has a higher false positive rate, overall accuracy, and detection rate.

SVM, Spam, Naïve-Bayes algorithm, SVM, Linear Classifiers, Margin Classification

"Intelligent Email Filtering Using Support Vector Machine for Spam Detection", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 7, page no.b315-b322, July-2025, Available :http://www.jetir.org/papers/JETIR2507138.pdf

"Intelligent Email Filtering Using Support Vector Machine for Spam Detection", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 7, page no. ppb315-b322, July-2025, Available at : http://www.jetir.org/papers/JETIR2507138.pdf