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Commit History
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Stop tracking cloned source corpora; keep derived CSVs only
1e4d129 -
Build train/val/test splits with template grouping; document dataset and leakage controls
b624ee7 -
Add ExAIS loader and fraud/marketing triage; ExAIS spam is telco bulk, not fraud
c4f6a67
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Add SMS collection schema, labelling guide and validator
d69d436 -
Add length experiment: model classifies document shape, not fraud
23c24d1 -
Add Nigerian SMS evaluation set, eval harness and failure analysis: 80% accuracy vs 99.24% on public corpora
b0e32f1
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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 -
Day 2: add dataset download script and document data provenance
- Add scripts/download_data.py to fetch UCI SMS Spam, Enron-Spam, SpamAssassin, and Kaggle Fraudulent Email Corpus - Pin requests and kaggle in requirements.txt - Document dataset sources, sizes, and licenses in data/raw/README.md - Raw data files remain gitignored; pipeline is reproducible via the script81f6cf7 -
Initialize Wilma project structure and dependencies
bbd7f6a