README.rst

|PyPI pyversions|

mljar-api-python

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

from mljar import Mljar

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

model.predict(X)

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 setup.py install

How to use it

1. Create an account at mljar.com 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 mljar.com 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 contact@mljar.com.

Examples

The examples are here!.

Testing

To run tests with command:

python -m tests.run