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A series of Python Jupyter notebooks that help you better understand "The Elements of Statistical Learning" book
| Date | Stars |
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| 2026-07-24 | 927 |
| 2026-07-25 | 927 |
| 2026-07-28 | 927 |
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# "The Elements of Statistical Learning" Notebooks Reproducing examples from the "The Elements of Statistical Learning" by Trevor Hastie, Robert Tibshirani and Jerome Friedman with Python and its popular libraries: **numpy**, **math**, **scipy**, **sklearn**, **pandas**, **tensorflow**, **statsmodels**, **sympy**, **catboost**, **pyearth**, **mlxtend**, **cvxpy**. Almost all plotting is done using **matplotlib**, sometimes using **seaborn**. ## Examples The documented Jupyter Notebooks are in the [examples](https://github.com/empathy87/The-Elements-of-Statistical-Learning-Python-Notebooks/tree/master/examples) folder: ### [examples/Mixture.ipynb](https://github.com/empathy87/The-Elements-of-Statistical-Learning-Python-Notebooks/blob/master/examples/Mixture.ipynb) Classifying the points from a mixture of "gaussians" using linear regression, nearest-neighbor, logistic regression with natural cubic splines basis expansion, neural networks, support vector machines, flexible discriminant analysis over MARS regression, mixture discriminant analysis, k-Means clustering, Gaussian mixture model and random forests.  ### [examples/Prostate Cancer.ipynb](https://github.com/empathy87/The-Elements-of-Statistical-Learning-Python-Notebooks/blob/master/examples/Prostate%20Cancer.ipynb) Predicting prostate specific antigen using ordinary least squares, ridge/lasso regularized linear regression, principal components regression, partial least squares and best subset regression. Model parameters are selected by K-folds cross-validation.  ### [examples/South African Heart Disease.ipynb](https://github.com/empathy87/The-Elements-of-Statistical-Learning-Python-Notebooks/blob/master/examples/South%20African%20Heart%20Disease.ipynb) Understanding the risk factors using logistic regression, L1 regularized logistic regression, natural cubic splines basis expansion for nonlinearities, thin-plate spline for mutual dependency, local logistic regression, kernel density estimation and gaussian mixture models.  ### [examples/Vowel.ipynb](https://github.com/empathy87/The-Elements-of-Statistical-Learning-Python-Notebooks/blob/master/examples/Vowel.ipynb) Vowel speech recognition using regression of an indicator matrix, linear/quadratic/regularized/reduced-rank discriminant analysis and logistic regression.  ### [examples/Bone Mineral Density.ipynb](https://github.com/empathy87/The-Elements-of-Statistical-Learning-Python-Notebooks/blob/master/examples/Bone%20Mineral%20Density.ipynb) Comparing patterns of bone mineral density relative change for men and women using smoothing splines.  ### [examples/Air Pollution Data.ipynb](https://github.com/empathy87/The-Elements-of-Statistical-Learning-Python-Notebooks/blob/master/examples/Air%20Pollution.ipynb) Analysing Los Angeles pollution data using smoothing splines.  ### [examples/Phoneme Recognition.ipynb](https://github.com/empathy87/The-Elements-of-Statistical-Learning-Python-Notebooks/blob/master/examples/Phoneme%20Recognition.ipynb) Phonemes speech recognition using reduced flexibility logistic regression.  ### [examples/Galaxy.ipynb](https://github.com/empathy87/T
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