📘 Lesson  ·  Lesson 94

Email Spam Detection

About this Project

💡 At a Glance

This project classifies messages as spam or not spam using a Naive Bayes model on text.

The Program

Python
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.naive_bayes import MultinomialNB

texts = ["win money now", "meeting at office", "free lottery prize", "project deadline today"]
labels = ["spam", "ham", "spam", "ham"]

vec = CountVectorizer()
X = vec.fit_transform(texts)
model = MultinomialNB()
model.fit(X, labels)

test = vec.transform(["win free prize"])
print("Prediction:", model.predict(test)[0])
Prediction: spam

How it Works

  • CountVectorizer turns text into word-count numbers.
  • Naive Bayes learns which words appear in spam vs ham, then predicts.

Summary

  • Convert text to numbers, train Naive Bayes, then predict spam/ham.
  • A practical introduction to text classification.
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