Email spam detection is an important application of machine learning and data mining that aims to identify and filter unsolicited, fraudulent, or unwanted emails. The increasing volume of spam emails poses significant challenges to individuals and organizations, including security threats, phishing attacks, and loss of productivity. This project proposes an email spam detection system that analyzes email content and classifies messages as either spam or legitimate (ham). Various text preprocessing techniques such as tokenization, stop-word removal, and feature extraction are applied to improve classification accuracy. Machine learning algorithms, including Naïve Bayes, Support Vector Machine (SVM), and Decision Trees, can be utilized to train the model on labeled email datasets. The performance of the system is evaluated using metrics such as accuracy, precision, recall, and F1-score. The proposed approach helps automate email filtering, reduces user effort, and enhances cybersecurity by minimizing the impact of spam and malicious emails
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