Statistical-Learning:

Notes and exercise attempts for "An Introduction to Statistical Learning"

Combined Github Repository

I have combined the two repository that is ISLR-python and stat-learning

R Directory Note:

• "(*)" means I am not sure about the answer
• Try out RStudio (www.RStudio.com) as an R IDE with the knitr package.
• Pull requests gladly accepted. If a pull request is too much effort, please at least file a new issue. :)
• Visit An Introduction to Statistical Learning Unofficial Solutions for an index of exercise solutions.

Python Directory Note:

• 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).

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

  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
• matplotlib
• seaborn
• statsmodels
• patsy

• 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 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!

• For an advanced treatment of these topics see Hastie et al. (2009)

Reference:

• James, G., Witten, D., Hastie, T., Tibshirani, R. (2013). An Introduction to Statistical Learning with Applications in R, Springer Science+Business Media, New York.
• Hastie, T., Tibshirani, R., Friedman, J. (2009). Elements of Statistical Learning, Second Edition, Springer Science+Business Media, New York.

Related resources

• The free video resources provided by BigData School
• free pdf download
• official web
• http://www.statlearning.com
• http://statlearning.class.stanford.edu/