https://github.com/empathy87/the-elements-of-statistical-learning-python-notebooks

A series of Python Jupyter notebooks that help you better understand "The Elements of Statistical Learning" book
https://github.com/empathy87/the-elements-of-statistical-learning-python-notebooks

data-analysis data-science machine-learning python sklearn statistical-learning tensorflow tutorials

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A series of Python Jupyter notebooks that help you better understand "The Elements of Statistical Learning" book

• Host: GitHub
• URL: https://github.com/empathy87/the-elements-of-statistical-learning-python-notebooks
• Owner: empathy87
• Created: 2019-01-23T03:30:18.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2021-07-18T17:48:43.000Z (about 3 years ago)
• Last Synced: 2024-09-23T06:32:57.714Z (10 days ago)
• Topics: data-analysis, data-science, machine-learning, python, sklearn, statistical-learning, tensorflow, tutorials
• Language: Jupyter Notebook
• Homepage:
• Size: 57.6 MB
• Stars: 846
• Watchers: 22
• Forks: 259
• Open Issues: 3
• Metadata Files:
  • Readme: README.md

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README

        
# "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.

![alt](https://github.com/empathy87/The-Elements-of-Statistical-Learning-Python-Notebooks/blob/master/images/mixture.png)

### [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.

![alt](https://github.com/empathy87/The-Elements-of-Statistical-Learning-Python-Notebooks/blob/master/images/cancer.png)

### [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.

![alt](https://github.com/empathy87/The-Elements-of-Statistical-Learning-Python-Notebooks/blob/master/images/chd.png)

### [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.

![alt](https://github.com/empathy87/The-Elements-of-Statistical-Learning-Python-Notebooks/blob/master/images/vowel.png)

### [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.

![alt](https://github.com/empathy87/The-Elements-of-Statistical-Learning-Python-Notebooks/blob/master/images/bone.png)

### [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.

![alt](https://github.com/empathy87/The-Elements-of-Statistical-Learning-Python-Notebooks/blob/master/images/ozone_vs_pressure_gradient.png)

### [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.

![alt](https://github.com/empathy87/The-Elements-of-Statistical-Learning-Python-Notebooks/blob/master/images/phoneme.png)

### [examples/Galaxy.ipynb](https://github.com/empathy87/The-Elements-of-Statistical-Learning-Python-Notebooks/blob/master/examples/Galaxy.ipynb)

Analysing radial velocity of galaxy NGC7531 using local regression in multidimentional space.

![alt](https://github.com/empathy87/The-Elements-of-Statistical-Learning-Python-Notebooks/blob/master/images/galaxy.png)

### [examples/Ozone.ipynb](https://github.com/empathy87/The-Elements-of-Statistical-Learning-Python-Notebooks/blob/master/examples/Ozone.ipynb)

Analysing the factors influencing ozone concentration using local regression and trellis plot.

![alt](https://github.com/empathy87/The-Elements-of-Statistical-Learning-Python-Notebooks/blob/master/images/ozone.png)

### [examples/Spam.ipynb](https://github.com/empathy87/The-Elements-of-Statistical-Learning-Python-Notebooks/blob/master/examples/Spam.ipynb)

Detecting email spam using logistic regression, generalized additive logistic model, decision tree, multivariate adaptive regression splines, boosting and random forest.

![alt](https://github.com/empathy87/The-Elements-of-Statistical-Learning-Python-Notebooks/blob/master/images/spam.png)

### [examples/California Housing.ipynb](https://github.com/empathy87/The-Elements-of-Statistical-Learning-Python-Notebooks/blob/master/examples/California%20Housing.ipynb)

Analysing the factors influencing California houses prices using boosting over decision trees and partial dependance plots.

![alt](https://github.com/empathy87/The-Elements-of-Statistical-Learning-Python-Notebooks/blob/master/images/california.png)

### [examples/Demographics.ipynb](https://github.com/empathy87/The-Elements-of-Statistical-Learning-Python-Notebooks/blob/master/examples/Demographics.ipynb)

Predicting shopping mall customers occupation, and hence identifying demographic variables that discriminate between different occupational categories using boosting and market basket analysis.

![alt](https://github.com/empathy87/The-Elements-of-Statistical-Learning-Python-Notebooks/blob/master/images/demographics.png)

### [examples/ZIP Code.ipynb](https://github.com/empathy87/The-Elements-of-Statistical-Learning-Python-Notebooks/blob/master/examples/ZIP%20Code.ipynb)

Recognizing small hand-drawn digits using LeCun's Net-1 - Net-5 neural networks. 

![alt](https://github.com/empathy87/The-Elements-of-Statistical-Learning-Python-Notebooks/blob/master/images/zip1.png)

Analysing of the number three variation in ZIP codes using principal component and archetypal analysis.

![alt](https://github.com/empathy87/The-Elements-of-Statistical-Learning-Python-Notebooks/blob/master/images/zip2.png)

### [examples/Human Tumor Microarray Data.ipynb](https://github.com/empathy87/The-Elements-of-Statistical-Learning-Python-Notebooks/blob/master/examples/Human%20Tumor%20Microarray%20Data.ipynb)

Analysing microarray data using K-means clustring and hierarchical clustering. 

![alt](https://github.com/empathy87/The-Elements-of-Statistical-Learning-Python-Notebooks/blob/master/images/tumor.png)

### [examples/Country Dissimilarities.ipynb](https://github.com/empathy87/The-Elements-of-Statistical-Learning-Python-Notebooks/blob/master/examples/Country%20Dissimilarities.ipynb)

Analysing country dissimilarities using K-medoids clustering and multidimensional scaling.

![alt](https://github.com/empathy87/The-Elements-of-Statistical-Learning-Python-Notebooks/blob/master/images/country.png)

### [examples/Signature.ipynb](https://github.com/empathy87/The-Elements-of-Statistical-Learning-Python-Notebooks/blob/master/examples/Signature.ipynb)

Analysing signature shapes using Procrustes transformation.

![alt](https://github.com/empathy87/The-Elements-of-Statistical-Learning-Python-Notebooks/blob/master/images/signature.png)

### [examples/Waveform.ipynb](https://github.com/empathy87/The-Elements-of-Statistical-Learning-Python-Notebooks/blob/master/examples/Waveform.ipynb)

Recognizing wave classes using linear, quadratic, flexible (over MARS regression), mixture discriminant analysis and decision trees.

![alt](https://github.com/empathy87/The-Elements-of-Statistical-Learning-Python-Notebooks/blob/master/images/waveform.png)

### [examples/Protein Flow-Cytometry.ipynb](https://github.com/empathy87/The-Elements-of-Statistical-Learning-Python-Notebooks/blob/master/examples/Protein%20Flow%20Cytometry.ipynb)

Analysing protein flow-cytometry data using graphical-lasso undirected graphical model for continuous variables. 

![alt](https://github.com/empathy87/The-Elements-of-Statistical-Learning-Python-Notebooks/blob/master/images/cytometry.png)

### [examples/SRBCT Microarray.ipynb](https://github.com/empathy87/The-Elements-of-Statistical-Learning-Python-Notebooks/blob/master/examples/SRBCT%20Microarray.ipynb)

Analysing microarray data of 2308 genes and selecting the most significant genes for cancer classification using nearest shrunken centroids. 

![alt](https://github.com/empathy87/The-Elements-of-Statistical-Learning-Python-Notebooks/blob/master/images/srbct.png)

### [examples/14 Cancer Microarray.ipynb](https://github.com/empathy87/The-Elements-of-Statistical-Learning-Python-Notebooks/blob/master/examples/14%20Cancer.ipynb)

Analysing microarray data of 16,063 genes gathered by Ramaswamy et al. (2001) and selecting the most significant genes for cancer classification using nearest shrunken centroids, L2-penalized discriminant analysis, support vector classifier, k-nearest neighbors, L2-penalized multinominal, L1-penalized multinominal and elastic-net penalized multinominal. It is a difficult classification problem with p>>N (only 144 training observations).

### [examples/Skin of the Orange.ipynb](https://github.com/empathy87/The-Elements-of-Statistical-Learning-Python-Notebooks/blob/master/examples/Skin%20of%20the%20Orange.ipynb)

Solving a synthetic classification problem using Support Vector Machines and multivariate adaptive regression splines to show the influence of additional noise features.

### [examples/Radiation Sensitivity.ipynb](https://github.com/empathy87/The-Elements-of-Statistical-Learning-Python-Notebooks/blob/master/examples/Radiation%20Sensitivity.ipynb)

Assessing the significance of 12,625 genes from microarray study of radiation sensitivity using Benjamini-Hochberg method and the significane analysis of microarrays (SAM) approach.

![alt](https://github.com/empathy87/The-Elements-of-Statistical-Learning-Python-Notebooks/blob/master/images/radiation.png)