Spam Sentinel: Revolutionizing Email Protection Using Machine Learning Techniques

The rapid growth of email usage has brought about an unprecedented increase in spam emails, posing serious challenges to users and businesses alike. To tackle this issue, this study presents an enhanced spam email detection system leveraging the power of machine learning techniques. The objective is to create a robust and efficient model capable of accurately differentiating between legitimate emails and spam messages. Various features are extracted from the email content, including text-based characteristics, header information, and metadata, to create comprehensive representations of the emails. Machine learning algorithms, such as KNeighbors, Support Vector Classifier, Multinomial NB, Logistic Regression, Random Forest, Extra Trees Classifier, AdaBoost, Bagging, XGB Classifier, Decision Tree, Gradient Boosting, Gaussian NB (Naïve Bayes), Multinomial NB, Bernoulli NB (Naïve Bayes) and ensemble model are employed to develop and compare different spam detection models. The experimental results demonstrate the efficacy of the proposed enhanced spam email detection system. The combination of machine learning techniques leads to significant improvements in accuracy and efficiency, enabling effective identification and classification of spam emails. Bernoulli achieved better accuracy of 99%.