Naive Bayes can be extended to real-valued attributes, most commonly by assuming a Gaussian distribution. This extension of naive Bayes is called Gaussian Naive Bayes. Other functions can be used to estimate the distribution of the data, but the Gaussian (or Normal distribution) is the easiest to work with because you only need to estimate the mean and the standard deviation from your training data.

The Gaussian Naive Bayes is one classifier model. This works on the basis of Bayes Theorem. It is an algorithm having a Probabilistic Approach. It involves prior and posterior probability calculation of the classes in the dataset and the test data given a class respectively. It is not a single algorithm but a family of algorithms where all of them share a common principle, i.e. every pair of features being classified is independent of each other.

In this project, I have implemented Gaussian Naive Bayes on IRIS dataset. IRIS dataset consists of 50 samples from each of three species of Iris namely Iris setosa, Iris virginica and Iris versicolor. Four characteristics of each sample were mesaured: the length and the width of the sepals and petals, in centimeters. All these feature values are used for training the model. The name of each species associated with the sample is used as the output label which is the basic ingredient for supervised learning.