Language · machine learning
Catch the spam
🎓 School maths · Years 8–9Probability — Use how often words appear to weigh a likely outcome.Maths by year →
The Case. Your email hides junk mail automatically. How? You show a computer lots of spam and normal messages until it learns the difference.
Your tools. scikit-learn turns words into numbers (CountVectorizer) and learns a spam-detector (MultinomialNB).
Investigate. Press Run (the AI library loads the first time — give it a moment). Then it judges a brand-new message.
- Change
newto your own message — does it get caught? - It never saw your exact message — it learned patterns of spammy words.
your_code.py
Starting Python…
The Reveal
Starting Python… (first time only — a few seconds)
🗒️ Detective's notes
Bundles your findings, chart and code into one PDF you can keep, print or hand in.
🧩 How this code works
The tools it uses
- scikit-learn — a real AI toolkit that learns patterns from examples.
What this one does
- • It trains a model to learn from examples.
How to read it, top to bottom
- 1. Import — bring in the tools (listed above).
- 2. The data — the lists and numbers near the top. These are the bits you change.
- 3. The work — a little maths, or a loop that repeats a step.
- 4. Show it —
print(...)writes words;plt.show()draws the picture.
Use it for your own problems
- Change the numbers at the top and press Run — nothing breaks, so experiment!
- Ask your own version of the question — swap in your family, your scores, your week.
- The same tools work on any numbers — that's the superpower you're learning.
Need a hint?
The computer never memorises messages — it learns which words show up more in spam.