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Series of Data Science Graphs written by Philip Geurin and Matt Drury

Project description


# Autoregression

Here are the tools to automatically assess and test multiple working
machine learning techniques.


## Installation

A `setup.py` file is included. To install into a python environment run

```bash
pip install git+https://github.com/pgeurin/autoregression.git
```

Included are two extra modules:


##Galgraphs
Here you will find graphing functions for numpy arrays and pandas dataframes.

Graphs with ax take a axis from matplotlib.
Use pattern matplotlib 'fig, ax = subplots(1,1)' for best effect.

Graphs without an 'ax' input plot themselves.

The code used here HEAVILY relies upon the foundational work of Matt Drury.
This project just wouldn't be the same without it.
Pandas and matplotlib. are also foundational tools to the work.

How to use the documentation
----------------------------
Documentation in docstrings provided with the code.

We recommend exploring the docstrings using
`IPython <http://ipython.org/>`_, an advanced Python shell with
TAB-completion and introspection capabilities.

Use the built-in ``help`` function to view a function's docstring::

Available graphs:
---------------------
'emperical_distribution',
'one_dim_scatterplot',
'plot_emperical_distribution',
'plot_many_predicteds_vs_actuals',
'plot_many_residuals',
'plot_one_univariate',
'plot_solution_paths',
'plot_univariate_smooth',
'predicteds_vs_actuals',
'residual_plot',
'simple_indicator_specification',
'simple_spline_specification',
'standardize_y',
'train_test_split'

##Cleandata
Cleans pandas dataframes using modern machine learning practices.

Turn first to clean_df(). It's your friend in a world of darkness.
It detects all manner of unmentionable values and replaces them with the mean or
distinguishing feature.


## Versioning

0.0.1 - Working graphs.
0.0.2 - Documentation.
0.0.3 - More graphs.
0.0.4 - Cleaning. AutoRegression.

Project details


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0.0.4

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