This repository contains Python code for a selection of tables, figures and LAB sections from the book <A target="_blank" href='http://www-bcf.usc.edu/%7Egareth/ISL/index.html'>'An Introduction to Statistical Learning with Applications in R'</A> by James, Witten, Hastie, Tibshirani (2013).<P>

<IMG src='http://www-bcf.usc.edu/%7Egareth/ISL/ISL%20Cover%202.jpg', height=20%, width=20%> <P>

<A href='http://nbviewer.ipython.org/github/JWarmenhoven/ISL-python/blob/master/Notebooks/Chapter%203.ipynb'>Chapter 3 - Linear Regression</A><BR>

<A href='http://nbviewer.ipython.org/github/JWarmenhoven/ISL-python/blob/master/Notebooks/Chapter%204.ipynb'>Chapter 4 - Classification</A><BR>

<LI>numpy

<LI>scipy

<LI>scikit-learn

<LI>statsmodels

<LI>patsy

</UL>

It was a good way to learn more about Machine Learning in Python by creating these notebooks. I created some of the figures/tables of the chapters and worked through some LAB sections. At certain points I realize that it may look like I tried too hard to make the output identical to the tables and R-plots in the book. But I did this to explore some details of the libraries mentioned above (mostly matplotlib and seaborn). Note that this repository is <STRONG>not a tutorial</STRONG> and that you probably should have a copy of the book to follow along. Suggestions for improvement and help with unsolved issues are welcome!<P>