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/