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        "author": "Nikolay Lysenko",
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        "description": "[![Build Status](https://travis-ci.org/Nikolay-Lysenko/dsawl.svg?branch=master)](https://travis-ci.org/Nikolay-Lysenko/dsawl)\n[![codecov](https://codecov.io/gh/Nikolay-Lysenko/dsawl/branch/master/graph/badge.svg)](https://codecov.io/gh/Nikolay-Lysenko/dsawl)\n[![Maintainability](https://api.codeclimate.com/v1/badges/98fc23b8b51fb20f2920/maintainability)](https://codeclimate.com/github/Nikolay-Lysenko/dsawl/maintainability)\n[![PyPI version](https://badge.fury.io/py/dsawl.svg)](https://badge.fury.io/py/dsawl)\n\n# dsawl\n\n## What is it?\n\nThis is a set of tools for machine learning. Provided by the package utilities are described in the below table:\n\nSubject | Description | Docs\n:-----: | :---------: | :--:\nActive Learning | Highly-modular system that recommends which previously unlabelled examples should be labelled in order to increase model quality quickly and significantly. Special features: various options for both exploitation and exploration. | [Read more](https://github.com/Nikolay-Lysenko/dsawl/blob/master/docs/active_learning_demo.ipynb)\nStacking | A method that applies machine learning algorithm to out-of-fold predictions or transformations made by other machine learning models. Special features: support of any `sklearn`-compatible estimators (in particular, pipelines). | [Read more](https://github.com/Nikolay-Lysenko/dsawl/blob/master/docs/stacking_demo.ipynb)\nTarget Encoding | An alternative to one-hot encoding and hashing trick that attempts to have both memory efficiency and incorporation of all useful information from initial features. Special features: `sklearn`-compatible wrapper that can transform data out-of-fold and apply an estimator to the result.| [Read more](https://github.com/Nikolay-Lysenko/dsawl/blob/master/docs/target_encoding_demo.ipynb)\n\nRepository name is a combination of three words: DS, saw, and awl. DS is as an abbreviation for Data Science and the latter two words represent useful tools.\n\n\n## How to install the package?\n\nThe package is compatible with Python 3.5 or newer. A virtual environment where it is guaranteed that the package works can be created based on [the file](https://github.com/Nikolay-Lysenko/dsawl/blob/master/requirements.txt) named `requirements.txt`.\n\nTo install a stable release of the package, run this command:\n```\npip install dsawl\n```\n\nTo install the latest version from sources, execute this from your terminal:\n```\ncd path/to/your/destination\ngit clone https://github.com/Nikolay-Lysenko/dsawl\ncd dsawl\npip install -e .\n```\n\nIf you have any troubles with installation, your questions are welcome.",
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