Chapter 2 – Phishing Domain Detection

1. The following are some text-cleaning tasks to perform:
   • Clean your texts of stopwords, digits, and punctuation marks.
   • Perform lemmatization.
2. Create a word dictionary, including their frequencies.
3. Remove the non-words from the dictionary.
4. Extract the features from the data.

Check Chapter2-Practice folder for the answers: https://github.com/PacktPublishing/Mastering-Machine-Learning-for-Penetration-Testing/tree/master/Chapter%202/Chaptre2-Practice.

5. Prepare the feature vectors and their labels.

train_labels = np.zeros(702)
train_labels[351:701] = 1
train_matrix = extract_features(train_dir)

6. Train the model with a linear support vector machine classifier.

model = LinearSVC()
model.fit(train_matrix,train_labels)

7. Print out the confusion matrix of your model.

result = model.predict(test_matrix)
print (confusion_matrix(test_labels,result...