Paper Title
Categorization of Email using Machile Learning Algorithm

Abstract
This project investigates a comparison between 2 completely different approaches for classifying emails supported their classes. Naive Thomas Bayes and Hidden Markov Model (HMM).Two completely different machine learning algorithms, each are used for detection whether or not AN email is vital or spam. Naive Thomas Bayes Classifier relies on conditional possibilities, it's quick and works nice with little data set. It considers freelance words as a feature. HMM could be a generative, probabilistic model that gives North American nation with distribution over the sequences of observations. HMM's will handle inputs of variable length and facilitate programs return to the foremost possible call, supported each previous selections and current information. Varied mixtures of IP techniques- stop words removing, stemming, summarizing are tried on each the algorithms to examine the variations in accuracy additionally on notice the simplest methodology among them. Keywords - Email Classification, Hidden Markov Model, Naive Bayes, Natural Language Processing, NLTK, Supervised Learning