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* remove references to old package name
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* changed example notebook name
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Taylor Miller committed Feb 4, 2017
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2 changes: 1 addition & 1 deletion Example1.py
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"""This file is used to create and compare two models on a particular dataset.
It provides examples of reading from both csv and SQL Server. Note that this
example can be run as-is after installing HCPyTools. After you have
example can be run as-is after installing healthcare.ai. After you have
found that one of the models works well on your data, move to Example2
"""
from healthcareai import DevelopSupervisedModel
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6 changes: 3 additions & 3 deletions README.rst
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- If you plan on deploying a model (ie, pushing predictions to SQL Server), run this in SSMS beforehand:

.. _healthcare.ai: http://healthcare.ai/py/
.. _notebook: notebooks/HCPyToolsExample1.ipynb
.. _notebook: notebooks/Example1.ipynb
.. code-block:: sql
CREATE TABLE [SAM].[dbo].[HCPyDeployClassificationBASE] (
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.. _create a post in our Google Group: https://groups.google.com/forum/#!forum/healthcareai-users

Contributing
=============
============
You want to help? Wohoo! We welcome that and are willing to help newbies get started.

First, See `here`_ for instructions on setting up your development environment

.. _here: https://github.com/HealthCatalystSLC/HCPyTools/blob/master/CONTRIBUTING.rst
.. _here: https://github.com/HealthCatalystSLC/healthcareai-py/blob/master/CONTRIBUTING.rst

After that's done, *here's the contribution workflow:*

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8 changes: 4 additions & 4 deletions docs/FAQ.rst
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Who is this project for?
########################

While data scientists in healthcare will likely find this project valuable, the target audience for HCTools are those BI developers, data architects, and SQL developers that would love to create appropriate and accurate models with healthcare data. While existing machine learning packages are certainly irreplaceable, we think that there is a set of data problems specific to healthcare that warrant new tools.
While data scientists in healthcare will likely find this project valuable, the target audience for healthcare.ai are those BI developers, data architects, and SQL developers that would love to create appropriate and accurate models with healthcare data. While existing machine learning packages are certainly irreplaceable, we think that there is a set of data problems specific to healthcare that warrant new tools.

How does HCRTools focus on healthcare?
######################################
How does healthcare.ai focus on healthcare?
###########################################

HCTools differs from other machine learning packages in that it focuses on data issues specific to healthcare. This means that we pay attention to longitudinal questions, offer an easy way to do risk-adjusted comparisons, and provide easy connections and deployment to databases.
healthcare.ai differs from other machine learning packages in that it focuses on data issues specific to healthcare. This means that we pay attention to longitudinal questions, offer an easy way to do risk-adjusted comparisons, and provide easy connections and deployment to databases.

Who started this project?
#########################
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2 changes: 1 addition & 1 deletion docs/develop.rst
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Expand Up @@ -10,7 +10,7 @@ What is ``DevelopSupervisedModel``?

- To jump straight to an example notebook, see `here`_

.. _here: https://github.com/HealthCatalystSLC/healthcareai-py/blob/master/notebooks/HCPyToolsExample1.ipynb
.. _here: https://github.com/HealthCatalystSLC/healthcareai-py/blob/master/notebooks/Example1.ipynb

Am I ready for model creation?
##############################
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4 changes: 2 additions & 2 deletions docs/index.rst
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.. HCPyTools documentation master file, created by
.. healthcare.ai documentation master file, created by
sphinx-quickstart on Wed Oct 19 16:34:01 2016.
You can adapt this file completely to your liking, but it should at least
contain the root `toctree` directive.
Welcome to the healthcare.ai documentation!
-------------------------------------------------
-------------------------------------------

The purpose of this python package is to streamline healthcare machine learning. It does this by including functionality specific to healthcare, as well as simplifying the workflow of creating and deploying models.

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