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CITES ML

Machine Learning with the CITES Trade Database

Installation

Install Python 3.6, jupyter notebook, and run pip install -r requirements.txt

Aims

This repo is an exploratory effort to see how Machine Learning can be applied to the CITES Trade Database, with three main objectives and experiments, each represented in a jupyter notebook:

  1. Given a partial permit, can we figure out what category it belongs to?
  2. Given some trade data over time, can we predict future trade numbers, per country and per species?
  3. Given valid and invalid permits (raw data before validation steps), can we learn the sanity checks and validation processes to spot invalid permits automatically?