I want to train a binary classification algorithm for spam detection using labeled data set. The dataset has the following features:
Email address, text message (split into subject and corpus), date
An example of data is:
Email | Subject | Corpus | Date [email protected] | Example | this is just an example of my dataset | 2020/08/20
What I would like is to transform data features in real numbers and binarize email addresses. As algorithm I was thinking of SVM and/or Naïve Bayes.
My difficulties are, however, in how transform data features in real numbers in order to get more parameters in my classifier.
I am using Python.
Could you please give me an example of how to do it?