Let's write a spam classifier using Naive Bayes. You're going to be surprised how easy this is. In fact, most of the work ends up just being reading all the input data that we're going to train on and actually parsing that data in. The actual spam classification bit, the machine learning bit, is itself just a few lines of code. So that's usually how it works out: reading in and massaging and cleaning up your data is usually most of the work when you're doing data science, so get used to the idea!
import os
import io
import numpy
from pandas import DataFrame
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.naive_bayes import MultinomialNB
def readFiles(path):
for root, dirnames, filenames in os.walk(path):
for filename in filenames:
path = os...