Problem 3: Manually Apply Bayes' Theorem for Classification

Problem Description

Write a Python program that manually calculates the Naive Bayes classification score for two classes, given prior probabilities and feature likelihoods (without using Scikit-learn), and determines the predicted class.

Input

Prior probabilities for two classes, and likelihood values for two features under each class.

Output

The calculated score for each class, and the predicted class.

Constraints

• All probabilities are between 0 and 1.

Example Input

text
P(Spam)=0.4, P(NotSpam)=0.6
P(free|Spam)=0.6, P(free|NotSpam)=0.1
P(meeting|Spam)=0.05, P(meeting|NotSpam)=0.3
(email contains "free", not "meeting")

Example Output

text
Score(Spam): 0.228
Score(NotSpam): 0.042
Predicted class: Spam

Concepts Covered

Manual probability multiplication, Bayes' Theorem application.