Ham Among the Spam

With a growth in advertisements and cold-messaging we are now receiving a nonstop coherent threads of commercial messages and emails. A user, like you and I, sometimes find it difficult to find a text/email which is actually useful to us or the one which we seek. Detection systems such as Spam detection system are becoming increasingly useful to classify the important data amongst the bundle of raw and undesired data. In this post we'll look at one such detection model, a spam detection model using NLP (natural language processing) and also learn about classification using Naïve Bayes. You can see that we are interested in calculating the posterior probability of P(h d) from the prior probability p(h) with P(D) and P(d h). UCI have an available data set of more than 5000 mixed text messages, click here.