Categorization of Email using Machile Learning Algorithm
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 techniquesstop
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