Custom Naive Bayes Classifier

Source: https://github.com/keaganderson/naiveBayes

Description: A project done for the class TCSS 455, Intro to Machine Learning. This is a Naive Bayes classification made from scratch that can be used to classify data. It does this by waiting until a query has been made (a lazy classification) then running an algorithm that calculates the percentage needed. To test it, it uses the Spambase Dataset to create a machine learning classification on the probability of text being spam depending on its contents. The effectiveness of the classifier is calculated with a Confusion Matrix and an Auc Roc Plot. Then a Naive Bayes implementation is created from the SKLearn library to compare.