In this video, I walk through how support vector machines work in a visual way, and then go step by step through how to write a Python script to use SVMs to classify muffin and cupcake recipes.

In Part 1a, I visually define the following terms:

• Margin
• Support vectors
• Hyperplane

In Part 1b, I go through the following steps in a Jupyter Notebook:

• Import libraries (pandas, numpy, sklearn, matplotlib)
• Import data
• Prepare the data
• Fit the model
• Visualize results
• Predict a new case

In Part 2, I talk about ways to tune the model:

• Higher dimensions
• Multiple classes
• C parameter
• Kernel trick (RBF with gamma)

In Part 3, I talk about the pros and cons of SVM.

You can find all of my code and data on Github: https://github.com/adashofdata

Subscribe: https://www.youtube.com/channel/UCyv-PL-QgkAXEfDRcKrYMeA/featured

#python #machine-learning