Ahsanul Haque's Projects
The polls app from the official Django tutorial, that demonstrates how to build data-driven Python apps in Azure App Service.
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.
Efficient Multistream Classification using Direct DensIty Ratio Estimation
Python notebooks with ML and deep learning examples with Azure Machine Learning | Microsoft
A sample Django app using PostgreSQL for the Azure App Service Web App + Database tutorial
MultiStream Regression
PySpark Cheat Sheet - example code to help you learn PySpark and develop apps faster
SAND: Semi-Supervised Adaptive Novel Class Detection and Classification over Data Stream