Introduction to Statistical Learning Solutions (Python)

This book (authored by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani ) is an excellent introduction to the data science and machine learning feild. Particularly in developing an analytical foundation and writing code to solve common data science problems. Below are my solutions to the problems throughout the book. The book itself focuses on developing these models in R though, as python has become a cornerstone of the data science field I have approached the problems with python.

While I am confident in the answers provided I offer no guarantee that they are all correct. I would happily welcome corrections or suggestions.

Chapter 2: Statistical Learning

Conceptual Applied

Chapter 3: Linear Regression

Conceptual Applied

Chapter 4: Classification

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Chapter 5: Resampling Methods

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Chapter 6: Linear Model Selection and Regularization

Conceptual Applied

Chapter 7: Moving Beyond Linearity

Conceptual

Chapter 8: Tree-Based Models

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Chapter 9: Support Vector Machines

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Chapter 10: Unsupervised Learning

Conceptual Applied