From 44fefa8cfeee2a15b3a55f031d9952c32fc4ed53 Mon Sep 17 00:00:00 2001
From: Donne Martin <donne.martin@gmail.com>
Date: Sun, 14 Jun 2015 06:34:33 -0400
Subject: [PATCH] Added customer churn analysis.

---
 README.md            | 7 ++++---
 analyses/churn.ipynb | 2 +-
 2 files changed, 5 insertions(+), 4 deletions(-)

diff --git a/README.md b/README.md
index 5d126b3..593985c 100644
--- a/README.md
+++ b/README.md
@@ -19,7 +19,7 @@ This repo is a collection of IPython Notebooks I reference while working with da
 * [spark](#spark)
 * [mapreduce-python](#mapreduce-python)
 * [amazon web services](#aws)
-* [kaggle](#kaggle)
+* [kaggle-and-business-analyses](#kaggle-and-business-analyses)
 * [scikit-learn](#scikit-learn)
 * [pandas](#pandas)
 * [matplotlib](#matplotlib)
@@ -83,13 +83,14 @@ IPython Notebook(s) demonstrating Amazon Web Services (AWS) and AWS tools functi
   <img src="https://raw.githubusercontent.com/donnemartin/data-science-ipython-notebooks/master/images/kaggle.png">
 </p>
 
-## kaggle
+## kaggle-and-business-analyses
 
-IPython Notebook(s) used in [kaggle](https://www.kaggle.com/) competitions.
+IPython Notebook(s) used in [kaggle](https://www.kaggle.com/) competitions and business analyses.
 
 | Notebook | Description |
 |-------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------|
 | [titanic](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/kaggle/titanic.ipynb) | Predicts survival on the Titanic.  Demonstrates data cleaning, exploratory data analysis, and machine learning. |
+| [churn-analysis](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/analyses/churn.ipynb) | Predicts customer churn.  Exercises logistic regression, gradient boosting classifers, support vector machines, random forests, and k-nearest-neighbors.  Discussion of confusion matrices, ROC plots, feature importances, prediction probabilities, and calibration/descrimination.|
 
 <br/>
 <p align="center">
diff --git a/analyses/churn.ipynb b/analyses/churn.ipynb
index 04b0f4b..b2b5051 100644
--- a/analyses/churn.ipynb
+++ b/analyses/churn.ipynb
@@ -6,7 +6,7 @@
    "source": [
     "##Customer Churn##\n",
     "\n",
-    "Credits: Forked from [growth-workshop](https://github.com/aprial/growth-workshop)"
+    "Credits: Forked from [growth-workshop](https://github.com/aprial/growth-workshop) by [aprial](https://github.com/aprial), as featured on the [yhat blog](http://blog.yhathq.com/posts/predicting-customer-churn-with-sklearn.html)"
    ]
   },
   {