NLP Features for Phishing Email Detection

A full TF-IDF plus logistic regression phishing-email pipeline with bigrams, why ngram_range=(1,2) lets the model separately learn phrases like urgent action, a real chart of the actual top ten words a trained model learned to associate with spam, and the SMS Spam Collection benchmark.

~5 min total·4 quadrants of structured content

By the end of this topic, you will

Build a TF-IDF plus logistic regression pipeline for phishing-email text with bigrams enabled
Explain why ngram_range=(1,2) lets a model learn phrases, not just individual words, as signals
Inspect a trained model's coefficients to find the words it weighted most heavily toward spam

Quadrant 1 · e-Tutorial

Video lectures and walkthroughs

Video content coming soon.

Quadrant 2 · e-Content

Articles and case studies

NLP Features for Phishing Email Detection

Phishing lives or dies on language. This is where a real, trained model's own top ten spam-indicative words, claim, verify, immediately, winner, prize, come directly from code run against a real corpus, not chosen by hand.

Quadrant 3 · Web Resources