An Introduction to Statistical Learning 2nd edition is a textbook by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani. Conceptual and applied exercises are provided at the end of each chapter covering supervised learning(from chapter 1 to chapter 9),Overview of Deep Learning(chapter 10),Survival analysis(chapter 11),Unsupervised Learning(chapter 12), Multiple Testing(chapter 13).
This repository contains my solutions to the exercises as Jupyter Notebooks written in Python using:
- Numpy
- Pandas
- Matplotlib
- Seaborn
- itertools
- StatsModels
- Sklearn
- Patsy
- Pygam
- dtreeviz
- Keras
- Tensorflow
- Lifelines
- Scipy
Links to view each notebook below. The code is provided here.
Chapter 2 - Statistical Learning
Chapter 5 - Resampling Methods
Chapter 6 - Linear Model Selection and Regularization
Chapter 7 - Moving Beyond Linearity
Chapter 8 - Tree-Based Methods
Chapter 9 - Support Vetor Machines
Chapter 11 - Survival Analysis and Censored Data
Chapter 12 - Unsupervised Learning
Running the notebooks enables you to execute the code and play around with any interactive features,and if you find something wrong inform me to make it correct.
Some of the libraries that are available in R are not avaliable in python so i used the closest library to solve the exercices.