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Python wrapper over MLJAR API

Project description

|Build Status| |PyPI version| |Coverage Status| |PyPI pyversions|


A simple python wrapper over mljar API. It allows MLJAR users to create
Machine Learning models with few lines of code:

.. code:: python

from mljar import Mljar

model = Mljar(project='My awesome project', experiment='First experiment'),y)


That's all folks! Yeah, I know, this makes Machine Learning super easy!
You can use this code for following Machine Learning tasks: \* Binary
classification (your target has only two unique values) \* Regression
(your target value is continuous) \* More is coming soon!

How to install

You can install mljar with **pip**:


pip install -U mljar

or from source code:


python install

How to use it

1. Create an account at and login.
2. Please go to your users settings (top, right corner).
3. Get your token, for example 'exampleexampleexample'.
4. Set environment variable ``MLJAR_TOKEN`` with your token value:


export MLJAR_TOKEN=exampleexampleexample

5. That's all, you are ready to use MLJAR in your python code!

What's going on?

- This wrapper allows you to search through different Machine Learning
algorithms and tune each of the algorithm.
- By searching and tuning ML algorithm to your data you will get very
accurate model.
- By calling method ``fit`` from ``Mljar class`` you create new project
and start experiment with models training. All your results will be
accessible from your account - this makes Machine Learning
super easy and keeps all your models and results in beautiful order.
So, you will never miss anything.
- All computations are done in MLJAR Cloud, they are executed in
parallel. So after calling ``fit`` method you can switch your
computer off and MLJAR will do the job for you!
- I think this is really amazing! What do you think? Please let us know
at ````.


The examples are `here! <>`__.


To run tests with command:


python -m

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