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:
- Generic XBRL to AI.
- XBRL in local GAAP including
- Danish GAAP og Danish IFRS (with extension) to AI
- German GAAP
- 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.
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
You can now run the test_xbrl_ai_dk.py.
Download the files and run the following command from the folder:
- Using pip:
>> pip install .
- Using setuptools:
>> python setup.py install