Addressing issue #604 : Adds effect plot visualizer

[naresh-bachwani]

This PR refers to issue #604. It adds effect plots to 'yellowbrick/regressors'.
It has the following features:

1. Auto identification of discrete and continuous columns.
2. Hyperparams for custom plots.

Sample Code and Plot

from yellowbrick.regressor.effect import EffectPlot
from yellowbrick.datasets import load_bikeshare
from sklearn.linear_model import LinearRegression
X, y = load_bikeshare()
viz = EffectPlot(LinearRegression(), colormap='cool')
viz.fit(X, y)
viz.poof(outpath = 'Effect.png')

TODOs and questions

Still to do:

• Provide parameter which allows users to define discrete and continuous features.
• Add test_effect.py
• Add Documentation.

Questions for the @DistrictDataLabs/team-oz-maintainers:

CHECKLIST

• Is the commit message formatted correctly?
• Have you noted the new functionality/bugfix in the release notes of the next release?

• Included a sample plot to visually illustrate your changes?
• Do all of your functions and methods have docstrings?
• Have you added/updated unit tests where appropriate?
• Have you updated the baseline images if necessary?
• Have you run the unit tests using pytest?
• Is your code style correct (are you using PEP8, pyflakes)?
• Have you documented your new feature/functionality in the docs?

• Have you built the docs using make html?