Build Naive Bayes spam classifier with F1
Company: Disney
Role: Machine Learning Engineer
Category: Machine Learning
Difficulty: medium
Interview Round: Technical Screen
You are given a text classification dataset for **spam detection** (binary labels: `spam` vs `not_spam`) in a Jupyter notebook environment.
### Task
1. Preprocess the text (basic cleaning/tokenization is sufficient).
2. Convert text to features suitable for Naive Bayes (e.g., bag-of-words or TF-IDF).
3. Train a **Naive Bayes** classifier.
4. Evaluate the model using **F1 score** (clearly state whether it is the F1 for the positive class or a specific averaging scheme).
5. Run the trained model on a few test examples and show predicted labels (and optionally probabilities).
### Constraints / Notes
- The dataset may be class-imbalanced.
- You should avoid data leakage (fit text vectorizer only on training data).
- You may choose reasonable train/validation splitting if only one labeled set is provided.
Overview: This question evaluates understanding of probabilistic text classification with Naive Bayes, text preprocessing and feature extraction, and use of the F1 score for performance measurement in a binary spam detection task.