Dash svm

apps/dash-svm/Procfile

@@ -0,0 +1 @@
+web: gunicorn --pythonpath apps/dash-svm app:server

apps/dash-svm/README.md

@@ -0,0 +1,73 @@
+# Support Vector Machine (SVM) Explorer [![Mentioned in Awesome Machine Learning](https://awesome.re/mentioned-badge.svg)](https://github.com/josephmisiti/awesome-machine-learning)
+
+This is a learning tool and exploration app made using the Dash interactive Python framework developed by [Plotly](https://plot.ly/).
+
+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](https://plot.ly/dash).
+
+Try out the [demo app here](https://dash-svm.plot.ly/).
+
+![alt text](images/screenshot.png "Screenshot")
+
+
+## Getting Started
+### Using the demo
+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.
+
+### Running the app locally
+
+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-sample-apps/apps/dash-svm.git
+cd dash-sample-apps/apps/dash-svm
+pip install -r requirements.txt
+```
+
+Run the app
+```
+python app.py
+```
+
+## About the app
+### How does it work?
+
+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.
+
+### What is an SVM?
+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](https://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf).
+
+## Built With
+* [Dash](https://dash.plot.ly/) - Main server and interactive components
+* [Plotly Python](https://plot.ly/python/) - Used to create the interactive plots
+* [Scikit-Learn](http://scikit-learn.org/stable/documentation.html) - Run the classification algorithms and generate datasets
+
+
+## Authors
+
+* **Xing Han Lu** - *Initial Work* - [@xhlulu](https://github.com/xhlulu)
+* **Matthew Chan** - *Code Review* - [@matthewchan15](https://github.com/matthewchan15)
+* **Yunke Xiao** - *Redesign* - [@YunkXiao](https://github.com/YunkeXiao)
+* **celinehuang** - *Code Review* - [@celinehuang](https://github.com/celinehuang)
+
+
+## Acknowledgments
+The heatmap configuration is heavily inspired from the [scikit-learn Classification Comparison Tutorial](http://scikit-learn.org/stable/auto_examples/classification/plot_classifier_comparison.html). Please go take a look!
+
+The idea of the [ROC Curve, the Matrix Pie Chart and Thresholding](https://github.com/nicolaskruchten/dash-roc) came from @nickruchten. The app would not have been as complete without his insightful advice.
+