Numer.ai is a hedge fund that hosts weekly online competitions for data science model building, allowing data science enthuasists a way to earn money by building models that help to explain phenomena in their sanitized data. A great part about working with Numer.ai data is that it’s already curated for you — no need to deal with wrangling, scraping, or cleaning. This is pretty awesome, as data cleaning is one of the more frustrating/boring parts of machine learning.
This repository uses the Numerai data from July 22nd and contains a base model built using the XGBoost algorithm.
Included is a Dockerfile that you can use to start up a container with Docker
To run through the analysis and experiment you can startup up the Jupyter notebook
$ jupyter notebook
If you want to run the python script by itself rather than a Jupyter notebook, run the following
$ python example_model.py