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        "author": "Adithya Balaji",
        "author_email": "adithyabsk@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 2 - Pre-Alpha",
            "Intended Audience :: Developers",
            "Intended Audience :: Education",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: Apache Software License",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7",
            "Topic :: Scientific/Engineering :: Artificial Intelligence",
            "Topic :: Software Development :: Libraries",
            "Topic :: Software Development :: Libraries :: Python Modules"
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        "description": "Foreshadow: Simple Machine Learning Scaffolding\n===============================================\n\n|BuildStatus| |DocStatus| |Coverage| |CodeStyle| |License|\n\nForeshadow is an automatic pipeline generation tool that makes creating, iterating,\nand evaluating machine learning pipelines a fast and intuitive experience allowing\ndata scientists to spend more time on data science and less time on code.\n\n.. |BuildStatus| image:: https://dev.azure.com/georgianpartners/foreshadow/_apis/build/status/georgianpartners.foreshadow?branchName=master\n   :target: https://dev.azure.com/georgianpartners/foreshadow/_build/latest?definitionId=1&branchName=master\n\n.. |DocStatus| image:: https://readthedocs.org/projects/foreshadow/badge/?version=latest\n  :target: https://foreshadow.readthedocs.io/en/latest/?badge=latest\n  :alt: Documentation Status\n\n.. |Coverage| image:: https://img.shields.io/azure-devops/coverage/georgianpartners/foreshadow/1.svg\n  :target: https://dev.azure.com/georgianpartners/foreshadow/_build/latest?definitionId=1&branchName=master\n  :alt: Coverage\n\n.. |CodeStyle| image:: https://img.shields.io/badge/code%20style-black-000000.svg\n  :target: https://github.com/ambv/black\n  :alt: Code Style\n\n.. |License| image:: https://img.shields.io/badge/License-Apache%202.0-blue.svg\n  :target: https://github.com/georgianpartners/foreshadow/blob/master/LICENSE\n  :alt: License\n\nInstalling Foreshadow\n---------------------\n\n.. code-block:: console\n\n    $ pip install foreshadow\n\nRead the documentation to `set up the project from source`_.\n\n.. _set up the project from source: https://foreshadow.readthedocs.io/en/development/developers.html#setting-up-the-project-from-source\n\nGetting Started\n---------------\n\nTo get started with foreshadow, install the package using pip install. This will also\ninstall the dependencies. Now create a simple python script that uses all the\ndefaults with Foreshadow.\n\nFirst import foreshadow\n\n.. code-block:: python\n\n    import foreshadow as fs\n\nAlso import sklearn, pandas, and numpy for the demo\n\n.. code-block:: python\n\n    import pandas as pd\n\n    from sklearn.datasets import boston_housing\n    from sklearn.model_selection import train_test_split\n\nNow load in the boston housing dataset from sklearn into pandas dataframes. This\nis a common dataset for testing machine learning models and comes built in to\nscikit-learn.\n\n.. code-block:: python\n\n    boston = load_boston()\n    bostonX_df = pd.DataFrame(boston.data, columns=boston.feature_names)\n    bostony_df = pd.DataFrame(boston.target, columns=['target'])\n\nNext, exactly as if working with an sklearn estimator, perform a train test\nsplit on the data and pass the train data into the fit function of a new Foreshadow\nobject\n\n.. code-block:: python\n\n    X_train, X_test, y_train, y_test = train_test_split(bostonX_df,\n       bostony_df, test_size=0.2)\n    shadow = fs.Foreshadow()\n    shadow.fit(X_train, y_train)\n\nNow `fs` is a fit Foreshadow object for which all feature engineering has been\nperformed and the estimator has been trained and optimized. It is now possible to\nutilize this exactly as a fit sklearn estimator to make predictions.\n\n.. code-block:: python\n\n    shadow.score(X_test, y_test)\n\nGreat, you now have a working Foreshaow installation! Keep reading to learn how to\nexport, modify and construct pipelines of your own.\n\nKey Features\n------------\n- Automatic Feature Engineering\n- Automatic Model Selection\n- Rapid Pipeline Development / Iteration\n- Automatic Parameter Optimization\n- Ease of Extensibility\n- Scikit-Learn Compatible\n\nForeshadow supports python 3.6+\n\nDocumentation\n-------------\n`Read the docs!`_\n\n.. _Read the docs!: https://foreshadow.readthedocs.io/en/development/index.html\n",
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        "keywords": "feature,machine,learning,automl,foreshadow",
        "license": "Apache-2.0",
        "maintainer": "Adithya Balaji",
        "maintainer_email": "adithyabsk@gmail.com",
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