Binary classification: how to transform features in real numbers? 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 
Gg12_school@gmail.com | 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?
The term you are looking for is text classification. There exists a huge number of tutorials and papers out there, for example this tutorial  and this survey.