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Showing content from https://github.com/mscaudill/IntroStatLearn below:

mscaudill/IntroStatLearn: Exercises from 'Introduction to Statistical Learning with Applications in R' written in Python.

Python code for Labs and Applied questions from the book: 'Introduction to Statistical Learning with Applications in R' by James, Witten, Hastie and Tibshirani (2013).

This well-written book provides an excellent introduction to statistical learning methods. The algorithms and datasets used in the book are written in R. Since I use python for data analysis, I decided to rewrite the labs and answer the applied questions using python and the following packages:

Numpy
Scipy
Pandas
Scikit-learn
Statsmodels
Patsy
Matplotlib

I have also converted the R datasets into csv files. These can be found in the data directory.

The code for each lab or applied question is written in a Jupyter notebook. For the applied questions, there is no guarantee that the solutions are correct. Suggestions and corrections are always welcome.

• Chapter 2: Statistical Learning
• Chapter 3: Linear Regression
• Chapter 4: Classification
• Chapter 5: Resampling Methods
• Chapter 6: Linear Model Selection & Regularization
• Chapter 7: Moving Beyond Linearity
• Chapter 8: Tree-Based Methods
• Chapter 9:
• Chapter 10:

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