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Support Vector Machines in Python from Start to Finish

StatQuest with Josh Starmer via YouTube

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Overview

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This course demonstrates an end-to-end SVM classification workflow in Python with scikit-learn, using an RBF kernel and credit-default data. It covers missing-data handling, downsampling, feature encoding and scaling, parameter optimization, and interpretation of the final decision boundary.

Syllabus

This webinar was recorded 20200609 at am New York Time
Awesome song and introduction
Import Modules
Import Data
Missing Data Part 1: Identifying
Missing Data Part 2: Dealing with it
Downsampling the data
Format Data Part 1: X and y
Format Data Part 2: One-Hot Encoding
Format Data Part 3: Centering and Scaling
Build a Preliminary SVM
Optimize Parameters with Cross Validation GridSearchCV
Build and Draw Final SVM

Taught by

StatQuest with Josh Starmer

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