This project is a <b>Naive-Bayes</b> classifier written in python. 

<b>Naive Bayes classifier</b> is a supervised learning technique based on <b>Bayes Theorem</b>.

It assumes all features of a feature vector to be independent of each other.

The feature vector belongs to the class which has <b>maximum posterior probability</b>:

<b>Posterior Probability = (Likelihood * Prior Probability)/(Evidence)</b>

Wikipedia Description:

"A naive Bayes classifier is a simple probabilistic classifier based on applying Bayes' theorem
with strong (naive) independence assumptions. A more descriptive term for the underlying
probability model would be "independent feature model".


THINGS TO DO:
1) LAPLACIAN SMOOTHING