SPAM EMAIL DETECTION

This paper presents the design and implementation of SpamGuard AI, an advanced security framework for automated email classification and threat detection. The system integrates Multinomial Naive Bayes machine learning algorithms with Natural Language Processing (NLP) to identify malicious communication patterns. By leveraging the Enron dataset for training and TF-IDF Vectorization for feature extraction, the system achieves a high classification accuracy of 97.4%. Beyond basic filtering, the proposed solution incorporates VADER Sentiment Analysis and Regexbased URL scanning to deliver context-aware security verdicts. Experimental results demonstrate that the system effectively mitigates phishing risks with minimal latency.