Figures, Tables and Problems from the book 'An Introduction to Statistical Learning with Applications in R' by James, Witten, Hastie, Tibshirani (2013). Using Python 3.x.

List of Chapters:

• 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

Dependencies:

• pandas
• numpy
• scipy
• scikit-learn
• statsmodels
• patsy
• matplotlib
• seaborn
• pyGAM
• pydot and graphviz (to plot decission trees)
• scikit-plot (to plot ROC for classification)

I obtained the data from https://github.com/JWarmenhoven/ISLR-python.

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. https://web.stanford.edu/~hastie/ElemStatLearn/