Email Security: Predictive Analysis of Spam Detection Using Machine Learning
TL;DR
The proposed system utilizes textual features such as word frequency, message structure, and content patterns to classify emails as spam or legitimate (ham) through supervised learning techniques, and is developed using Python and Scikit-learn.
Abstract
Email communication is one of the most significant modes of digital interaction, playing a central role in business communication, personal correspondence, and information exchange across the modern world. The increasing volume of unsolicited, deceptive, and malicious emails, commonly known as spam, has made accurate spam detection a challenging task for email service providers, organizations, and individual users. Traditional filtering methods primarily depend on static rule-based approaches and manually curated blacklists, which often fail to adapt to rapidly evolving spam patterns and result in inaccurate classification. Recent advancements in Artificial Intelligence (AI) and Machine Learning (ML) have enabled the development of intelligent systems capable of analyzing email content and generating accurate spam classifications using data-driven approaches digital communication. The proposed system utilizes textual features such as word frequency, message structure, and content patterns to classify emails as spam or legitimate (ham) through supervised learning techniques. The email dataset undergoes preprocessing procedures, including text cleaning, tokenization, stop-word removal, stemming, feature extraction using TF-IDF vectorization, and train-test splitting before model training. The predictive model is developed using Python and Scikit-learn, while Flask is employed to create a web-based interface that allows users to enter email content and obtain real-time spam classification.