balaka-18/svm_visual_tool

A simple web app that helped students visualize the SVM algorithm according to their choice of hyperparameter setting.

SVM VISUAL TOOL : A VISUALIZATION TOOL TO HELP STUDENTS UNDERSTAND SUPPORT VECTOR MACHINES BETTER.

VISIT THE WEB APP HERE : Welcome to the SVM Visual Tool(SVM VT)

YOUTUBE VIDEO LINK : Watch it here - Support Vector Machine Visualizer

Support Vector Machines is one of the most famous supervised learning algorithms in Data Science. However, understanding how hyperparameters can affect the performance of this algorithm is quite tricky for a beginner.

When I had started learning SVM, I had found it difficult to imagine or picturize how the decision boundary and threshold lines change when I change the values of the hyperparameters or switch between kernels.

So, I decided to make a simple web app that helped students visualize the SVM algorithm according to their choice of hyperparameter setting.

About the web app :

The web app consists of two pages :

1. The HOME page : The home page is basically the gateway to the visual tool. It also consists a link that redirects the user to blogs on SVM, in case the user wants to have a short read.

HOME PAGE SCREENSHOT :

2. The main page : The SVM VISUAL TOOL

This page contains the main plot that is generated upon changing the settings below the plot on the left hand side.

THE PLOTS :

THE CONTROLS AND THE SHORT NOTES TO EXPLAIN THE HYPERPARAMETERS :

Tech stack : Frontend : HTML, CSS, Bootstrap, Flask.

Backend : Dash, Python.

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