Email Spam Classification

Abstract

Email spam continues to be a significant challenge in digital communication, often leading to productivity loss, data breaches, and security threats. Traditional rule-based spam filters are static and ineffective against constantly evolving spam techniques. This project presents a machine learning-based email spam classification system that leverages natural language processing (NLP) for efficient and accurate detection. Using TF-IDF vectorizationthe system extracts meaningful features from email text. Several supervised learning algorithms, including Naïve Bayes, Logistic Regression, and Support Vector Machine (SVM), are trained and evaluated to identify spam with high precision. The trained model is integrated into a lightweight web interface using Streamlit, allowing users to input or upload email content and receive instant classification results. The system demonstrates high accuracy and adaptability, offering a scalable and real-time solution for modern spam detection challenges