StarMirror
/
data-science-ipython-notebooks
mirror of https://github.com/donnemartin/data-science-ipython-notebooks
1
0
Fork 0
Code Issues Projects Releases Wiki Activity

Reorder sections in README

pull/37/head
Donne Martin 2016-07-31 08:20:51 -04:00
parent b2da6e6704
commit 07e6273279
1 changed files with 15 additions and 15 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

30
README.md
View File

@ -12,8 +12,8 @@
## Index
* [scikit-learn](#scikit-learn)
* [kaggle-and-business-analyses](#kaggle-and-business-analyses)
* [scikit-learn](#scikit-learn)
* [deep-learning](#deep-learning)
* [statistical-inference-scipy](#statistical-inference-scipy)
* [pandas](#pandas)
@ -31,6 +31,20 @@
* [contact-info](#contact-info)
* [license](#license)
<br/>
<p align="center">
<img src="https://raw.githubusercontent.com/donnemartin/data-science-ipython-notebooks/master/images/kaggle.png">
</p>
## kaggle-and-business-analyses
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) | Predict survival on the Titanic. Learn 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) | Predict customer churn. Exercise logistic regression, gradient boosting classifers, support vector machines, random forests, and k-nearest-neighbors. Includes discussions of confusion matrices, ROC plots, feature importances, prediction probabilities, and calibration/descrimination.|
<br/>
<p align="center">
<img src="https://raw.githubusercontent.com/donnemartin/data-science-ipython-notebooks/master/images/scikitlearn.png">
@ -52,20 +66,6 @@ IPython Notebook(s) demonstrating scikit-learn functionality.
| [gmm](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/scikit-learn/scikit-learn-gmm.ipynb) | Implement Gaussian mixture models in scikit-learn. |
| [validation](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/scikit-learn/scikit-learn-validation.ipynb) | Implement validation and model selection in scikit-learn. |
<br/>
<p align="center">
<img src="https://raw.githubusercontent.com/donnemartin/data-science-ipython-notebooks/master/images/kaggle.png">
</p>
## kaggle-and-business-analyses
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) | Predict survival on the Titanic. Learn 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) | Predict customer churn. Exercise logistic regression, gradient boosting classifers, support vector machines, random forests, and k-nearest-neighbors. Includes discussions of confusion matrices, ROC plots, feature importances, prediction probabilities, and calibration/descrimination.|
<br/>
<p align="center">
<img src="http://i.imgur.com/ZhKXrKZ.png">