ahhaque

ECHO

ECHO is a semi-supervised framework for classifying evolving data streams based on our previous approach SAND. The most expensive module of SAND is the change detection module, which has cubic time complexity. ECHO uses dynamic programming to reduce the time complexity. Moreover, ECHO has a maximum allowable sliding window size. If there is no concept drift detected within this limit, ECHO updates the classifiers and resets the sliding window. Experiment results show that ECHO achieves significant speed up over SAND while maintaining similar accuracy. Please refer to the paper (mentioned in the reference section) for further details.

SAND

SAND: Semi-Supervised Adaptive Novel Class Detection and Classification over Data Stream

FUSION

Efficient Multistream Classification using Direct DensIty Ratio Estimation

MSR

MultiStream Regression

djangoapp

The polls app from the official Django tutorial, that demonstrates how to build data-driven Python apps in Azure App Service.

MachineLearningNotebooks

Python notebooks with ML and deep learning examples with Azure Machine Learning | Microsoft

MLOpsPython

msdocs-django-postgresql-sample-app

A sample Django app using PostgreSQL for the Azure App Service Web App + Database tutorial

pyspark-cheatsheet

PySpark Cheat Sheet - example code to help you learn PySpark and develop apps faster