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https://github.com/jwarmenhoven/islr-python

An Introduction to Statistical Learning (James, Witten, Hastie, Tibshirani, 2013): Python code
https://github.com/jwarmenhoven/islr-python

islr islr-python machine-learning predictive-modeling statistical-learning

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An Introduction to Statistical Learning (James, Witten, Hastie, Tibshirani, 2013): Python code

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# ISLR-python

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



 For **Bayesian data analysis** using PyMC3, 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.

https://www.statlearning.com/



James, G., Witten, D., Hastie, T., Tibshirani, R. (2021). An Introduction to Statistical Learning with Applications in  R, Second Edition,  Springer Science+Business Media, New York.

https://www.statlearning.com/



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/