the-elements-of-statistical-learning

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My notes and codes (jupyter notebooks) for the "The Elements of Statistical Learning" by Trevor Hastie, Robert Tibshirani and Jerome Friedman

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- 2.3-least-squares-and-nearest-neighbors.ipynb
- 2.4-statistical-decision-theory.ipynb
- 2.5-local-methods-in-high-dimensions.ipynb
- 2.6-statistical-models-supervised-learning-and-function-approximation.ipynb
- 2.7-structured-regression-models.ipynb
- 2.8-classes-of-restricted-estimators.ipynb
- 2.9-model-selection-and-the-bias-variance-tradeoff.ipynb
- exercise-solutions.ipynb
- 3.1-introduction.ipynb
- 3.2-linear-regression-models-and-least-squares.ipynb
- 3.2.1-example-prostate-cancer.ipynb
- 3.2.2-the-gauss-markov-theorem.ipynb
- 3.2.3-multiple-regression-from-simple-multivariate-regression.ipynb
- 3.2.4-multiple-outputs.ipynb
- 3.3-subset-selection.ipynb
- 3.4-shrinkage-methods.ipynb
- 3.4.1-ridge-regression.ipynb
- 3.4.2-the-lasso.ipynb
- 3.4.4-discussion-subset-selection-ridge-regression-and-the-lasso.ipynb
- 3.4.4-least-angle-regression.ipynb
- 3.5-methods-using-derived-input-directions.ipynb
- 4.1-introduction.ipynb
- 4.2-linear-regression-of-an-indicator-matrix.ipynb
- 4.3-linear-discriminant-analysis.ipynb
- 4.3.1-regularized-discriminant-analysis.ipynb
- 4.3.2-computations-for-LDA.ipynb
- 4.3.3-reduced-rank-linear-discriminant-analysis.ipynb
- 4.4-logistic-regression.ipynb
- 4.4.1-fitting-logistic-regression-models.ipynb
- 4.4.2-example-south-african-heart-disease.ipynb
- 4.4.3-quadratic-approximations-and-inference.ipynb
- 4.4.4-L-1-regularized-logistic-regression.ipynb
- - fig-11.10.png
- 11.7-example-zip-code-data.ipynb
- - prostate.data
  - prostate.info
- - SAheart.data
  - SAheart.info.txt
- - .DS_Store
  - zip.test
  - zip.train
- .DS_Store
.gitignore
LICENSE
README.md
requirements.txt

# Installation Guide

1. Get the code

git clone https://github.com/maitbayev/the-elements-of-statistical-learning

Downloads the entire project code from GitHub to your computer.

cd the-elements-of-statistical-learning

Moves into the project folder you just downloaded.

2. Python

Easy Recommended

Prerequisites

Git Needed to download the project code from GitHub.
Python 3 On Windows, be sure to check 'Add Python to PATH' during installation.

pip install -r requirements.txt

Installs the Python libraries listed in requirements.txt (or similar).

jupyter notebook

Launches Jupyter in your browser so you can open and run the notebook (.ipynb) files.

✅ If it runs without errors and prints output in the terminal, it worked.