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@article{189613,
        author = {Wakchaure Vaishnavi Nandkishor and Rohom Pratiksha Shubham and Jadhav Rajashri Narayan and Jadhav Priyanka Vilas},
        title = {Spam Email Detection Using Machine Learning Algorithms},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {7},
        pages = {6801-6803},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=189613},
        abstract = {Spam emails are unwanted messages that flood user inboxes, consuming time and resources. Machine Learning (ML) techniques provide automated ways to classify emails as spam or ham (legitimate). This research compares popular ML algorithms Naive Bayes, Support Vector Machine (SVM), Random Forest, and Logistic Regression using text features extracted from emails. Results show that combining advanced feature extraction with ensemble models improves detection accuracy. The system can be used to build efficient email filters.},
        keywords = {Spam Detection, Machine Learning, Classification, Text Mining, Email Security},
        month = {December},
        }