Copyright © 2025 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{164274,
        author = {Prof Rajendra Arakh and Arjit Kumar and Aryan Mishra and Anshul Singh Patel and Astha Srivas},
        title = {Spam Detection Using Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {10},
        number = {12},
        pages = {1347-1354},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=164274},
        abstract = {Email spam is a growing problem, causing frustration for users and posing risks to their security. To combat this issue, researchers have turned to machine learning techniques like Naive Bayes. This study compares Naive Bayes with other methods like Support Vector Machine (SVM) to see which is better at spotting spam. Using datasets of spam and legitimate emails, the researchers tested Naive Bayes and SVM. They found that while both methods were effective, SVM had slightly higher accuracy, with Naive Bayes close behind. The study also discusses the challenges of spam detection and the importance of machine learning in addressing this issue. By comparing different methods, it provides valuable insights into how we can better protect against email spam.},
        keywords = {Spam detection, Machine learning, Naive Bayes, Support Vector Machine, Email security, Classification},
        month = {},
        }