Naive Bayes may sound too simple to be smart, but its logic is rooted in solid probability. In this post, we break down the core intuition behind the algorithm, explore how it handles real-world uncertainty, and explain why "naive" assumptions often lead to surprisingly accurate predictions.

Naive Bayes is a simple yet powerful classification algorithm based on Bayes’ Theorem. It's widely used in spam detection, sentiment analysis, and text classification. This post explains how it works, covers its main types (Gaussian, Multinomial, Bernoulli), and includes a Python implementation for beginners and data science learners.

This post explores the foundations of probability, including joint, marginal, and conditional probabilities using real-world examples like the Titanic dataset. We break down Bayes' Theorem and explain the intuition behind conditional probability, making complex ideas easy to grasp.