Building IoT Solutions with IoT Central

For those who have been working on IoT and IoT Edge, this is what we have all been waiting for. IoT Central offers a vehicle to streamline and automate the process, while shortening and accelerating your go-to-market. Edge computing has never been so easy. Ranga Vadlamudi (@RangaVadlamudi), a Microsoft Principal PM, who explained and demonstrated… Continue reading Building IoT Solutions with IoT Central

R Parallel Processing for Developing Ensemble Learning with SuperLearner

Ensemble learning which included multiple learners, i.e. Machine Learning algorithms, may take much longer time than expected to develop. When using a search grid for parameter optimization to train an ensemble, depending on the included algorithms, the number of variables, and the corresponding iterations based on combinations of parameter settings and with cross validation, it may take a… Continue reading R Parallel Processing for Developing Ensemble Learning with SuperLearner

A Shiny App for Monitoring Real-Time Data Stream

This is an app developed with Shiny and R for visualizing real-time data stream. In this recording, the app ran locally, while I downloaded raw data collected from a set of Raspberry Sensor Hat devices stored in Azure cloud storage to a local disk to mimic a data pipeline for acquiring data with a batch processing. In production,… Continue reading A Shiny App for Monitoring Real-Time Data Stream

Predicting House Price with Multiple Linear Regression

House Price Prediction This project was to develop a Machine Learning model for predicting a house price. Despite there were a number of tree-based algorithms relevant to this application, the project was to examine linear regression and focused on specifically four models: Linear Regression, Ridge Regression, Lasso Regression and Elastic Net. Overview Data Analysis Feature… Continue reading Predicting House Price with Multiple Linear Regression

Feature Selection with Help from Boruta

Why When developing a Machine Learning model, if there is a significant number of features to inspect, an initial and manual Exploratory Data Analysis may become tedious and nonproductive. One option is to facilitate the process by testing and identifying important variables based on statistical methods to help trim down features. And that is where… Continue reading Feature Selection with Help from Boruta