PROJECT OVERVIEW
How SpamGuard
thinks.
The project demonstrates how natural language processing and supervised learning can classify email text.
01 · PROBLEM
Why detect spam?
Spam emails can waste time, promote scams, and expose users to phishing attempts. Manually checking every message is inconvenient.
02 · AI TECHNIQUE
TF-IDF + Naive Bayes
TF-IDF converts words into numerical features. Naive Bayes uses patterns learned from labelled examples to predict a message category.
03 · WORKFLOW
Train, then predict
The sample dataset is split into training and test portions. The model learns from training text and evaluates predictions on held-out examples.
04 · LIMITATIONS
Not a security guarantee
This educational prototype uses a small sample dataset and text only. It cannot verify sender identity, inspect links, or guarantee that a message is safe.