#Extract Emails
tech = extract_message("C:\Users\Cody\Documents\Emails\tech.html")
gary = extract_message("C:\Users\Cody\Documents\Emails\gary.html")
gary2 = extract_message("C:\Users\Cody\Documents\Emails\gary2.html")
jesus = extract_message("C:\Users\Cody\Documents\Emails\Jesus.html")
jesus2 = extract_message("C:\Users\Cody\Documents\Emails\jesus2.html")
hockey = extract_message("C:\Users\Cody\Documents\Emails\hockey.html")
hockey2 = extract_message("C:\Users\Cody\Documents\Emails\hockey2.html")
shop = extract_message("C:\Users\Cody\Documents\Emails\shop.html")

#Build dictionary of features
count_vect = CountVectorizer()
x_train_counts = count_vect.fit_transform(news.data)

#Downscaling
tfidf_transformer = TfidfTransformer()
x_train_tfidf = tfidf_transformer.fit_transform(x_train_counts)
tf_transformer = TfidfTransformer(use_idf=False).fit(x_train_counts)
x_train_tf = tf_transformer.transform(x_train_counts)

#Train classifier
clf = MultinomialNB().fit(x_train_tfidf, news.target)

#List of the extracted emails
docs_new = [gary, gary2, jesus, jesus2, shop, tech, hockey, hockey2]

#Extract feautures from emails
x_new_counts = count_vect.transform(docs_new)
x_new_tfidf = tfidf_transformer.transform(x_new_counts)

#Predict the categories for each email
predicted = clf.predict(x_new_tfidf)

#Store Files in a category
hockey_emails = []
computer_emails = []
politics_emails = []
tech_emails = []
religion_emails = []
forsale_emails = []

#Print out results and store each email in the appropritate category list
for doc, category in zip(docs_new, predicted):
  print('%r ---> %s' % (doc, news.target_names[category]))
   if(news.target_names[category] == 'comp.sys.ibm.pc.hardware'):
        computer_emails.append(doc)
   if(news.target_names[category] == 'rec.sport.hockey'):
        hockey_emails.append(doc)
   if(news.target_names[category] == 'talk.politics.misc'):
       politics_emails.append(doc)
   if(news.target_names[category] == 'soc.religion.christian'):
       religion_emails.append(doc)
   if(news.target_names[category] == 'misc.forsale'):
       forsale_emails.append(doc)
   if(news.target_names[category] == 'comp.sys.ibm.pc.hardware'):
       computer_emails.append(doc)

print(hockey_emails)

output: ['hockey', 'hockey2']

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