nikhilkr29/Email-Spam-Classifier-using-Naive-Bayes

A Naive Bayes spam/ham classifier based on Bayes' Theorem. A bunch of email subject is first used to train the classifier and then a previously unseen email subject is fed to predict whether it is Spam or Ham.

Goal :

From a dataset containing various Subjects of emails which are classified as either Spam or as Ham (Not Spam).
We are required to predict whether given an Email subject whether it is Spam or Ham.

How we Implemented it :

We implemented it by first using CountVectorizer on the Text of email and after that we applied the MultinomialNB method from the NaiveBayes Classifier.

What is Naive Bayes:

It is a classification technique based on Bayes' Theorem with an assumption of independence among predictors. In simple terms, a Naive Bayes classifier assumes that the presence of a particular feature in a class is unrelated to the presence of any other feature.

Python Libraries used:

pandas, numpy, CountVectorizer and MultinomialNB from sklearn

What does CountVectorizer do:

Convert a collection of text documents to a matrix of token counts This implementation produces a sparse representation of the counts using scipy.sparse.csr_matrix. If you do not provide an a-priori dictionary and you do not use an analyzer that does some kind of feature selection then the number of features will be equal to the vocabulary size found by analyzing the data.