SMS Spam Detection Using NLP And Render Deployment

This study outlines the machine learning-driven approach utilizing Natural Language Processing (NLP) to automate classifying SMS messages as spam or legitimate, building upon prior research in SMS spam filtering. The escalating volume of unsolicited SMS messages necessitates efficient solutions to mitigate user annoyance and potential security threats, such as phishing and fraud. The proposed system effectively transforms raw text data into a high-dimensional feature space suitable for machine learning models by employing techniques such as tokenization, stop-word removal, stemming, and TF-IDF feature extraction. We evaluated several classification algorithms, including naive Bayes, Random Forest, and Support Vector Machines, demonstrating that Bernoulli naive Bayes achieved a commendable performance, with a precision of 94.54% and an accuracy of 96.42%. This system addresses the limitations of traditional rule-based filters by adapting to the dynamic nature of spamming techniques, thereby enhancing user experience and mitigating security risks associated with phishing and fraudulent messages. Furthermore, we deployed the trained model via Render, providing a user-friendly web interface for real-time spam classification. The system’s robustness is validated through comprehensive exploratory data analysis and rigorous model evaluation, demonstrating its potential for practical application in enhancing SMS communication security and efficiency.