devssh / svm

Learn to use Support Vector Machines in Python(sklearn) and R

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 <filename>

or

Run in RStudio

RBF SVM

Variation with C and Gamma

Tuning a linear SVM for Purpose

Running algorithms to find the best parameters

Principle Component Analysis

True Positive Rate vs False Positive Rate on Credit Card data

Precision Recall on Credit Card data

Sources

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

2. Kaggle datasets and competitions

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