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XBRL-AI

The purpuse off this project is to apply XBRL in AI and Machine learning, by usage of Python.

The project will be divided into 3 parts:

  1. Generic XBRL to AI.
  1. XBRL in local GAAP including
  1. Sample of Machine Learning implementation based on this project

Why this project?

Working with machine learning basicly comes down to one thing: y = f(X), y is what we want to predict, X is the input and f is the machine learning model. Unfortunately X hardly ever fits into to f. If we want to fit e.g. an XBRL-instance into to f, we need to prepare the data. XBRL needs a good representation to fit into AI and Machine learning.

Creating a good and standardized representation of XBRL into AI and Machine learning are the main purpose of this project.

Getting started

To see an example of how one could use xbrl-ai start by creating a conda environment from the cloned yaml file:

>> conda env create -f environment.yml

now activate the environment by using

>> activate xbrl_ai

for windows, or

>> source activate xbrl_ai

for linux.

You can now run the test_xbrl_ai_dk.py.

Installation

Download the files and run the following command from the folder:

>> pip install .
>> python setup.py install