A Python application reading receipts from the Quota for Exercising Parliamentary Activity (aka CEAP, from the Brazilian Chamber of Deputies) and outputs, for each of the receipts, a "probability of corruption" and a list of reasons why is considered this way.
- Fetch CEAP dataset from Chamber of Deputies
- Convert XML to CSV
- Translate CSVs to English
- Read CEAP dataset from CSV into Pandas
- Process in the "corruption pipeline"
- Monthly limits - quota
- Monthly limits - subquota
- Machine Learning models using scikit-learn
- Task to push to Jarbas via API
$ cd rosie
$ conda update conda
$ conda create --name serenata_rosie python=3
$ source activate serenata_rosie
$ ./setup
$ python src/rosie.py
A /tmp/serenata-data/irregularities.xz
file will be created. It's a compacted CSV with all the irregularities Rosie is able to find.