Build Naive Bayes spam classifier with F1

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.