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@article{206815,
        author = {Hanamant Klayani and Clitus Neil D Souza and Nandkumar and Nitish Patil and Shreesha K R},
        title = {SMS Spam Detection Using Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {no},
        pages = {551-554},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=206815},
        abstract = {The problem of spam SMS messages has been a significant issue to mobile users which has caused privacy, and fraud as well as unwanted nuisance. In order to solve this problem, the project primarily aims to create a system that can automatically classify and detect irrespective of SMS messages as spam or legitimate messages through machine learning techniques. The dataset (Ham + Spam) has been cleaned, the stop words have been removed, messages have been lemmatized and the TF-IDF vectorizer has transformed the messages into numbers to make them SMS ready. Multiple Machine learning models like Logistic Regression Classifier, Naïve Bayes Classifier, Decision Tree Classifier, Random Forest, Linear SVM, KNN and a Voting Ensemble Classifier will be trained & analysed. The performance of each model is assessed using various parameters such as accuracy, precision, recall, F1 score, confusion matrix, and ROC curve analysis. Linear SVM was the most successful model in SMS spam classification in the project as it was the most accurate of algorithms. The results show that ML-based approaches algorithm can be efficiency used to identify spam messages and assist users to increase their safety, avoid fraud, and improve the security of mobile communication.},
        keywords = {.},
        month = {July},
        }