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@ -64,7 +64,7 @@ IPython Notebook(s) used in [kaggle](https://www.kaggle.com/) competitions and b
| 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) | 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.|
| [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">
@ -277,7 +277,7 @@ IPython Notebook(s) demonstrating various command lines for Linux, Git, etc.
| [ruby](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/commands/misc.ipynb#ruby) | Used to interact with the AWS command line and for Jekyll, a blog framework that can be hosted on GitHub Pages. |
| [jekyll](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/commands/misc.ipynb#jekyll) | Simple, blog-aware, static site generator for personal, project, or organization sites. Renders Markdown or Textile and Liquid templates, and produces a complete, static website ready to be served by Apache HTTP Server, Nginx or another web server. |
| [pelican](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/commands/misc.ipynb#pelican) | Python-based alternative to Jekyll. |
| [django](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/commands/misc.ipynb#django) | High-level Python Web framework that encourages rapid development and clean, pragmatic design. It can be useful to share reports/analyses and for blogging. Lighter-weight alternatives include [Pyramid](https://github.com/Pylons/pyramid), [Flask](https://github.com/mitsuhiko/flask), [Tornado](https://github.com/tornadoweb/tornado), and [Bottle](https://github.com/bottlepy/bottle).
| [django](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/commands/misc.ipynb#django) | High-level Python Web framework that encourages rapid development and clean, pragmatic design. It can be useful to share reports/analyses and for blogging. Lighter-weight alternatives include [Pyramid](https://github.com/Pylons/pyramid), [Flask](https://github.com/pallets/flask), [Tornado](https://github.com/tornadoweb/tornado), and [Bottle](https://github.com/bottlepy/bottle).
## misc
@ -294,7 +294,7 @@ IPython Notebook(s) demonstrating miscellaneous functionality.
Anaconda is a free distribution of the Python programming language for large-scale data processing, predictive analytics, and scientific computing that aims to simplify package management and deployment.
Follow instructions to install [Anaconda](http://docs.continuum.io/anaconda/install.html) or the more lightweight [miniconda](http://conda.pydata.org/miniconda.html).
Follow instructions to install [Anaconda](https://docs.continuum.io/anaconda/install) or the more lightweight [miniconda](http://conda.pydata.org/miniconda.html).
### pip-requirements
@ -328,7 +328,7 @@ Notebooks tested with Python 2.7.x.
* [TensorFlow Tutorials](https://github.com/pkmital/tensorflow_tutorials) by Parag K Mital
* [Summer School 2015](https://github.com/mila-udem/summerschool2015) by mila-udem
* [Kaggle](https://www.kaggle.com/)
* [Yhat Blog](http://blog.yhathq.com/)
* [Yhat Blog](http://blog.yhat.com/)
## contributing