This is a demo of the Dash interactive Python framework developed by Plotly.

Dash abstracts away all of the technologies and protocols required to build an interactive web-based application and is a simple and effective way to bind a user interface around your Python code. To learn more check out our documentation.

Try out the demo app here.

This demo lets you interactive explore Support Vector Machine (SVM).

It includes a few artificially generated datasets that you can choose from the dropdown, and that you can modify by changing the sample size and the noise level of those datasets.

The other dropdowns and sliders lets you change the parameters of your classifier, such that it could increase or decrease its accuracy.

First create a virtual environment with conda or venv inside a temp folder, then activate it.

virtualenv dash-svm-venv

# Windows
dash-svm-venv\Scripts\activate
# Or Linux
source venv/bin/activate

Clone the git repo, then install the requirements with pip

git clone https://github.com/plotly/dash-svm.git
cd dash-svm
pip install -r requirements.txt

Run the app

python app.py

This app is fully written in Dash + scikit-learn. All the components are used as input parameters for scikit-learn functions, which then generates a model with respect to the parameters you changed. The model is then used to perform predictions that are displayed on a contour plot, and its predictions are evaluated to create the ROC curve and confusion matrix.

In addition to creating models, scikit-learn is used to generate the datasets you see, as well as the data needed for the metrics plots.

An SVM is a popular Machine Learning model used in many different fields. You can find an excellent guide to how to use SVMs here.

• Dash - Main server and interactive components
• Plotly Python - Used to create the interactive plots
• Scikit-Learn - Run the classification algorithms and generate datasets

Please read CONTRIBUTING.md for details on our code of conduct, and the process for submitting pull requests to us.

• Xing Han Lu - Initial Work - @xhlulu

See also the list of contributors who participated in this project.

This project is licensed under the MIT License - see the LICENSE.md file for details