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        "author": "Philip Geurin and Matt Drury",
        "author_email": "philip.geurin@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 3 - Alpha",
            "Intended Audience :: Developers",
            "Intended Audience :: Education",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: BSD License",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.3",
            "Programming Language :: Python :: 3.4",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Topic :: Scientific/Engineering",
            "Topic :: Scientific/Engineering :: Mathematics"
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        "description": "\n# Autoregression\n\nHere are the tools to automatically assess and test multiple working\nmachine learning techniques.\n\n\n## Installation\n\nA `setup.py` file is included. To install into a python environment run\n\n```bash\npip install git+https://github.com/pgeurin/autoregression.git\n```\n\nIncluded are two extra modules:\n\n\n##Galgraphs\nHere you will find graphing functions for numpy arrays and pandas dataframes.\n\nGraphs with ax take a axis from matplotlib.\nUse pattern matplotlib 'fig, ax = subplots(1,1)' for best effect.\n\nGraphs without an 'ax' input plot themselves.\n\nThe code used here HEAVILY relies upon the foundational work of Matt Drury.\nThis project just wouldn't be the same without it.\nPandas and matplotlib. are also foundational tools to the work.\n\nHow to use the documentation\n----------------------------\nDocumentation in docstrings provided with the code.\n\nWe recommend exploring the docstrings using\n`IPython <http://ipython.org/>`_, an advanced Python shell with\nTAB-completion and introspection capabilities.\n\nUse the built-in ``help`` function to view a function's docstring::\n\nAvailable graphs:\n---------------------\n 'emperical_distribution',\n 'one_dim_scatterplot',\n 'plot_emperical_distribution',\n 'plot_many_predicteds_vs_actuals',\n 'plot_many_residuals',\n 'plot_one_univariate',\n 'plot_solution_paths',\n 'plot_univariate_smooth',\n 'predicteds_vs_actuals',\n 'residual_plot',\n 'simple_indicator_specification',\n 'simple_spline_specification',\n 'standardize_y',\n 'train_test_split'\n\n##Cleandata\nCleans pandas dataframes using modern machine learning practices.\n\nTurn first to clean_df(). It's your friend in a world of darkness.\nIt detects all manner of unmentionable values and replaces them with the mean or\ndistinguishing feature.\n\n\n## Versioning\n\n0.0.1 - Working graphs.\n0.0.2 - Documentation.\n0.0.3 - More graphs.\n0.0.4 - Cleaning. AutoRegression.\n",
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        "home_page": "https://github.com/pgeurin/autoregression",
        "keywords": "statistics",
        "license": "BSD",
        "maintainer": "",
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        "name": "autoregression",
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        "summary": "Series of Data Science Graphs written by Philip Geurin and Matt Drury",
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