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