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Day 4: clean all datasets and produce unified train/val/test splits
- Add HTML stripping to cleaners.py (with 2 new tests, 8 total passing) - Add email_utils.py with RFC-822 multipart parsing - Add cleaners for Enron-Spam, SpamAssassin, and Kaggle 419 corpus - Add merge_datasets.py producing stratified 80/10/10 splits - Pin scikit-learn in requirements.txt - Document processed dataset schema and reproduction steps Final dataset: 39,465 rows, 48.24% scam, balanced across all splits2aa5ce2 -
Initialize Wilma project structure and dependencies
bbd7f6a