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My Python code for labs and exercises in the book "An Introduction to Statistical Learning with Applications in R".
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My Python coding for labs and applied exercises in the book An Introduction to Statistical Learning with Applications in R by James, Witten, Hastie, Tibshirani.

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

Development environment:

  • Anaconda 4.3.1 for macOS, with Python 3.6
  • Jupyter Notebook 5.0.0
  • Emacs 25.1 with Emacs IPython Notebook

Python libraries used:

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

Reference: Elements of Statistical Learning by Hastie, T., Tibshirani, R., Friedman, J.

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