We delve into the intricacies of Gaussian Naive Bayes classification. The focus is on determining the probability of a data point belonging to a specific class among several, emphasizing probabilistic assessment over precise labeling. The article breaks down key concepts, from Bayesian decision theory to Bayes' theorem, and provides a step-by-step implementation using the Iris dataset.