The main goal of this project is to develop a smart spam detection system that can accurately classify incoming emails using machine learning techniques. Email spam is a major cybersecurity issue that disrupts user productivity and often carries phishing or malware risks. Traditional spam filters rely on basic keyword rules that are easy for attackers to bypass. This project addresses that limitation by training classification models, specifically Decision Tree and Support Vector Machines (SVM) on labeled email datasets. These models learn to recognize patterns in spam and non-spam emails, such as word frequency, structure, and metadata. By the end of the project, students are expected to deliver a working classifier that can be tested and deployed to identify spam emails in real-time environments, improving cybersecurity awareness and reducing information overload.
This project follows a twelve-week schedule, starting with foundational understanding of machine learning and Python programming. In the first two weeks, students will gather email datasets and set up the development environment using Python, Anaconda, or Google Colab. They will explore relevant libraries such as Scikit-learn, Pandas, and NLTK for data preprocessing and feature extraction.
During the middle weeks, students will build and train classification models using decision trees and SVM. They will test the model against new datasets and fine-tune hyperparameters to improve accuracy. As the project progresses, efforts will focus on refining prediction results, reducing false positives and negatives, and developing a user-friendly interface if applicable. In the final weeks, documentation and a team presentation will be completed. Although the system may not be foolproof against evolving spam tactics, it will serve as a valuable proof of concept in spam filtering and machine learning application.
