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The online course based on the book has been moved to EDX. 
Link now points to https://www.edx.org/school/stanfordonline
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README.md

ISLR-python

This repository contains Python code for a selection of tables, figures and LAB sections from the book 'An Introduction to Statistical Learning with Applications in R' by James, Witten, Hastie, Tibshirani (2013).

For Bayesian data analysis, take a look at this repository.

2018-01-15:
Minor updates to the repository due to changes/deprecations in several packages. The notebooks have been tested with these package versions. Thanks @lincolnfrias and @telescopeuser.

2016-08-30:
Chapter 6: I included Ridge/Lasso regression code using the new python-glmnet library. This is a python wrapper for the Fortran library used in the R package glmnet.

Chapter 3 - Linear Regression
Chapter 4 - Classification
Chapter 5 - Resampling Methods
Chapter 6 - Linear Model Selection and Regularization
Chapter 7 - Moving Beyond Linearity
Chapter 8 - Tree-Based Methods
Chapter 9 - Support Vector Machines
Chapter 10 - Unsupervised Learning

Extra: Misclassification rate simulation - SVM and Logistic Regression

This great book gives a thorough introduction to the field of Statistical/Machine Learning. The book is available for download (see link below), but I think this is one of those books that is definitely worth buying. The book contains sections with applications in R based on public datasets available for download or which are part of the R-package ISLR. Furthermore, there is a Stanford University online course based on this book and taught by the authors (See course catalogue for current schedule).

Since Python is my language of choice for data analysis, I decided to try and do some of the calculations and plots in Jupyter Notebooks using:

  • pandas
  • numpy
  • scipy
  • scikit-learn
  • python-glmnet
  • statsmodels
  • patsy
  • matplotlib
  • seaborn

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 not a standalone tutorial and that you probably should have a copy of the book to follow along. Suggestions for improvement and help with unsolved issues are welcome! See Hastie et al. (2009) for an advanced treatment of these topics.

References:

James, G., Witten, D., Hastie, T., Tibshirani, R. (2013). An Introduction to Statistical Learning with Applications in R, Springer Science+Business Media, New York. http://www-bcf.usc.edu/~gareth/ISL/index.html

Hastie, T., Tibshirani, R., Friedman, J. (2009). Elements of Statistical Learning, Second Edition, Springer Science+Business Media, New York. http://statweb.stanford.edu/~tibs/ElemStatLearn/

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