Naive Bayes Algorithm

What is Naive Bayes?

Naive Bayes means naive Bayes is a probability-based classification algorithm, often used in text classification.

In real programs, this topic helps in spam detection. Learn the idea first, then type the program yourself and compare the output.

Why should you learn this?

• It is useful for spam detection.
• It connects with sentiment classification.
• It improves your ability to read, write and debug Python programs.

Important Terms

These terms are used directly in this lesson. Understand them before memorising the code.

Syntax / Basic Pattern

The simple pattern is: prepare data, apply the concept, then show the result.

from sklearn.feature_extraction.text import CountVectorizer
from sklearn.naive_bayes import MultinomialNB
texts = ["win money", "hello friend", "free prize", "project meeting"]
y = ["spam", "ham", "spam", "ham"]
vectorizer = CountVectorizer()
X = vectorizer.fit_transform(texts)
model = MultinomialNB()
model.fit(X, y)

Complete Example Program

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

texts = ["win money", "hello friend", "free prize", "project meeting"]
y = ["spam", "ham", "spam", "ham"]

vectorizer = CountVectorizer()
X = vectorizer.fit_transform(texts)

model = MultinomialNB()
model.fit(X, y)
print(model.predict(vectorizer.transform(["free money"]))[0])

Expected Output

Program Explanation

• from sklearn.feature_extraction.text import CountVectorizer imports ready-made features from a module/library.
• from sklearn.naive_bayes import MultinomialNB imports ready-made features from a module/library.
• texts = ["win money", "hello friend", "free prize", "project meeting"] stores a value in texts.
• y = ["spam", "ham", "spam", "ham"] stores a value in y.
• vectorizer = CountVectorizer() stores a value in vectorizer.
• X = vectorizer.fit_transform(texts) stores a value in X.
• model = MultinomialNB() stores a value in model.

Where will you use it?

• Spam detection.
• Sentiment classification.
• Fast text classification.

Common Mistakes

• Training and testing the model on the same data.
• Using an algorithm without understanding the input features.
• Reporting only accuracy without checking actual mistakes and limitations.

Practice Tasks

1. Type the program in naive-bayes.py and run it.
2. Change input values or sample data and observe the new output.
3. Create one example related to spam detection.
4. Write 5 lines explaining the logic in your own words.

Summary

Naive Bayes is not a theory-only topic. You should be able to explain the meaning, write the example, run it successfully, and use it in a small practical program.