Update documentation

README.rst

@@ -3,19 +3,9 @@ foreshadow
 
 |License| |BuildStatus| |Coverage| |Code style: black|
 
-Manually running tests
-======================
-
--  setup pyenv for 3.5.5
--  setup pyenv for 3.6.5
--  setup a test pyenv virtualenv
-
-   -  install test_requirements to it
-
-   .. code:: bash
-
-      (testenvforeshadow) $ pip install -r test_requirements.txt
-      (testenvforeshadow) $ tox -r
+Foreshadow is an automatic pipeline generation tool that makes creating, iterating,
+and evaluating machine learning pipelines a fast and intuitive experience allowing
+data scientists to spend more time on data science and less time on code.
 
 .. |License| image:: https://img.shields.io/badge/License-Apache%202.0-blue.svg
    :target: https://github.com/georgianpartners/foreshadow/blob/master/LICENSE
@@ -24,4 +14,86 @@ Manually running tests
 .. |Coverage| image:: https://coveralls.io/repos/github/georgianpartners/foreshadow/badge.svg?branch=development
    :target: https://coveralls.io/github/georgianpartners/foreshadow
 .. |Code style: black| image:: https://img.shields.io/badge/code%20style-black-000000.svg
-   :target: https://github.com/ambv/black
+   :target: https://github.com/ambv/black
+
+Installing Foreshadow
+---------------------
+
+.. code-block:: console
+
+    $ pip install foreshadow
+
+Read the documentation to `set up the project from source`_.
+
+.. _set up the project from source: https://foreshadow.readthedocs.io/en/development/developers.html#setting-up-the-project-from-source
+
+Getting Started
+---------------
+
+To get started with foreshadow, install the package using pip install. This will also
+install the dependencies. Now create a simple python script that uses all the
+defaults with Foreshadow.
+
+First import foreshadow
+
+.. code-block:: python
+
+    import foreshadow as fs
+
+Also import sklearn, pandas, and numpy for the demo
+
+.. code-block:: python
+
+    import pandas as pd
+
+    from sklearn.datasets import boston_housing
+    from sklearn.model_selection import train_test_split
+
+Now load in the boston housing dataset from sklearn into pandas dataframes. This
+is a common dataset for testing machine learning models and comes built in to
+scikit-learn.
+
+.. code-block:: python
+
+    boston = load_boston()
+    bostonX_df = pd.DataFrame(boston.data, columns=boston.feature_names)
+    bostony_df = pd.DataFrame(boston.target, columns=['target'])
+
+Next, exactly as if working with an sklearn estimator, perform a train test
+split on the data and pass the train data into the fit function of a new Foreshadow
+object
+
+.. code-block:: python
+
+    X_train, X_test, y_train, y_test = train_test_split(bostonX_df,
+       bostony_df, test_size=0.2)
+    shadow = fs.Foreshadow()
+    shadow.fit(X_train, y_train)
+
+Now `fs` is a fit Foreshadow object for which all feature engineering has been
+performed and the estimator has been trained and optimized. It is now possible to
+utilize this exactly as a fit sklearn estimator to make predictions.
+
+.. code-block:: python
+
+    shadow.score(X_test, y_test)
+
+Great, you now have a working Foreshaow installation! Keep reading to learn how to
+export, modify and construct pipelines of your own.
+
+Key Features
+------------
+- Automatic Feature Engineering
+- Automatic Model Selection
+- Rapid Pipeline Development / Iteration
+- Automatic Parameter Optimization
+- Ease of Extensibility
+- Scikit-Learn Compatible
+
+Foreshadow supports python 3.5+
+
+Documentation
+-------------
+`Read the docs!`_
+
+.. _Read the docs!: https://foreshadow.readthedocs.io/en/development/index.html/