Email Spam Detection

Abstract

In my project, I set out to solve a problem we all face—spam emails. They’re not just irritating; they can also waste time, hide important messages, or even pose security risks. Since so much of our personal and professional communication happens over email, I wanted to build a smarter solution—one that works more like a human would, spotting unusual patterns, suspicious wording, or strange senders. That’s why I turned to machine learning: to create a system that could automatically detect and filter spam with more accuracy and less effort from the user. I trained the system using thousands of labeled emails, teaching it to understand the difference between spam and genuine messages. I used logistic regression—a widely used and effective machine learning algorithm—to classify the emails. I also applied natural language processing (NLP) to understand the content of the messages better. To improve the model and communicate results more clearly, I used data  visualization tools to track performance, accuracy, and how well the system adapted to different types of spam. Unlike basic filters with fixed rules, this model improves over time, learning from patterns and making smarter predictions.