Published Paper Details:

: Email spam refers to the distribution of unsolicited and unwanted bulk messages via email, affecting individuals, businesses, and organizations globally. Its purpose is to promote products, services, or fraudulent activities, often using deceptive tactics to entice recipients. Email spam can lead to inbox clutter, privacy risks, and security threats for individuals, while businesses may face productivity losses, cybersecurity risks, and reputational damage. To combat spam, various measures have been developed, including spam filters and email authentication protocols. However, spammers continuously adapt their techniques, necessitating ongoing advancements in email security. Mitigating the impact of email spam requires effective detection and prevention mechanisms, along with user awareness and caution. Maintaining a secure and reliable email environment remains crucial in the face of this persistent challenge. To effectively combat this issue, supervised machine learning algorithms have been widely employed, with Naive Bayes, Support Vector Machines (SVM), and Random Forest being among the most popular choices. However, a comprehensive comparative analysis is necessary to determine the most effective algorithm for accurately identifying spam emails. This research paper presents a detailed comparative analysis of Naive Bayes, SVM, and Random Forest in terms of their performance metrics, computational efficiency, and ease of implementation for email spam detection. The study aims to provide valuable insights and guidance for researchers and practitioners in selecting the optimal approach for effective spam detection.