https://github.com/devssh/svm

Learn to use Support Vector Machines in Python(sklearn) and R
https://github.com/devssh/svm

anaconda credit-card-fraud cross-validation grid-search ipython-notebook jupyter-notebook kaggle matplotlib mushroom-classification pandas pyplot python r rbf rstudio sklearn support-vector-machines svm

Last synced: 6 months ago
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Learn to use Support Vector Machines in Python(sklearn) and R

• Host: GitHub
• URL: https://github.com/devssh/svm
• Owner: devssh
• Created: 2017-07-22T17:21:56.000Z (about 8 years ago)
• Default Branch: master
• Last Pushed: 2017-08-16T14:26:57.000Z (about 8 years ago)
• Last Synced: 2025-03-24T12:11:32.583Z (7 months ago)
• Topics: anaconda, credit-card-fraud, cross-validation, grid-search, ipython-notebook, jupyter-notebook, kaggle, matplotlib, mushroom-classification, pandas, pyplot, python, r, rbf, rstudio, sklearn, support-vector-machines, svm
• Language: Jupyter Notebook
• Homepage:
• Size: 30.6 MB
• Stars: 11
• Watchers: 2
• Forks: 25
• Open Issues: 0
• Metadata Files:
  • Readme: Readme.md

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README

          
The Interactive Python notebooks can be opened using tools like Jupyter Notebooks or by opening .ipynb files in Github/browser

For Python3

Install the required libraries using Pip such as 


```

pip install sklearn

pip install pandas

pip install numpy

pip install matplotlib

```

For R

Install the required libraries using install.packages or RStudio


Run the files using 


```

R -f 

``` 

or 

Run in RStudio

RBF SVM


![SVM Radial Basis Function RBF](https://cdn.rawgit.com/devssh/svm/022af90c/RBF.svg)

Variation with C and Gamma


![SVM variations with C and Gamma](https://cdn.rawgit.com/devssh/svm/022af90c/EffectOfGammaAndC.svg)

Tuning a linear SVM for Purpose


![SVC Types](https://cdn.rawgit.com/devssh/svm/04014479/SVC%20Types.svg)

Running algorithms to find the best parameters


![Grid Search](https://github.com/devssh/svm/blob/master/GridSearchResults.png)

![Grid Search](https://github.com/devssh/svm/blob/master/Mushroom%20dataset.png)

Principle Component Analysis


![Plot the data](https://cdn.rawgit.com/devssh/svm/65703b62/Principle%20Component%20Analysis.svg)

![Kaggle Credit Card Fraud](https://github.com/devssh/svm/blob/master/Credit%20card.png)

True Positive Rate vs False Positive Rate on Credit Card data


![TPR FPR](https://cdn.rawgit.com/devssh/svm/ae81ffd4/TPR-FPR%20Curve.svg)

Precision Recall on Credit Card data


![Precision Recall](https://cdn.rawgit.com/devssh/svm/ae81ffd4/PrecisionRecallCurve.svg)

Sources

1. Dummy data and practise from Coursera - Machine Learning - University of Michigan

2. Kaggle datasets and competitions




 Fork this on Github to make changes!