StarMirror/data-science-ipython-notebooks
1
0
Fork 0
mirror of https://github.com/donnemartin/data-science-ipython-notebooks.git synced 2024-03-22 13:30:56 +08:00
Code Issues Projects Releases Wiki Activity

Added customer churn analysis.

Browse Source
This commit is contained in:
Donne Martin 2015-06-14 06:34:33 -04:00
parent 9a7522e1be
commit 44fefa8cfe
2 changed files with 5 additions and 4 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

7
README.md
View File

@ -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">

2
analyses/churn.ipynb
View File

@ -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)"
]
},
{