Build your first ML classifier that distinguishes normal web requests from attacks using TF-IDF vectorization and Logistic Regression.

• Text vectorization with TF-IDF
• Training a binary classifier
• Evaluating model performance
• Understanding the decision boundary

python classify.py

Training samples: 800
Test samples: 200

Classification Report:
              precision    recall  f1-score   support
      NORMAL       0.95      0.97      0.96       150
      ATTACK       0.89      0.84      0.86        50

Accuracy: 0.94