https://github.com/balaka-18/svm_flask
A simple web app that helped students visualize the SVM algorithm according to their choice of hyperparameter setting.
https://github.com/balaka-18/svm_flask
frontend homepage hyperparameters support-vector-machines svm webapp
Last synced: 3 months ago
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A simple web app that helped students visualize the SVM algorithm according to their choice of hyperparameter setting.
- Host: GitHub
- URL: https://github.com/balaka-18/svm_flask
- Owner: BALaka-18
- License: mit
- Created: 2020-08-30T03:56:46.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2020-09-23T21:09:23.000Z (over 5 years ago)
- Last Synced: 2025-06-30T00:40:13.296Z (7 months ago)
- Topics: frontend, homepage, hyperparameters, support-vector-machines, svm, webapp
- Language: HTML
- Homepage:
- Size: 7.81 KB
- Stars: 2
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# SVM VISUAL TOOL : A VISUALIZATION TOOL TO HELP STUDENTS UNDERSTAND SUPPORT VECTOR MACHINES BETTER.
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.
VISIT THE WEB APP HERE : [Welcome to the SVM Visual Tool(SVM VT)](https://svm-main.herokuapp.com/)
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