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

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

Microsoft Cortana Intelligence Suite Workshop Video Tutorial Series (5/5): Predictive Web Service

The last part of this video tutorial series includes three exercises. First, Exercise 6 uses Power BI Desktop, import the summary data from the Spark cluster and create a report with drag-n-drop to visualize the data. Exercise 7 is the exciting part, configures and deploys a sample web app and configures it to consume the… Continue reading Microsoft Cortana Intelligence Suite Workshop Video Tutorial Series (5/5): Predictive Web Service

Microsoft Cortana Intelligence Suite Workshop Video Tutorial Series (4/5): Azure Spark Cluster

The objective of Exercise 5 is to create a table, then store and prepare summary data for later visualization. You will find out it is simple and straightforward using a Spark notebook to interactively work on an Azure Spark cluster. This video tutor series presents the live demonstrations of all the exercises to facilitate the… Continue reading Microsoft Cortana Intelligence Suite Workshop Video Tutorial Series (4/5): Azure Spark Cluster

Microsoft Cortana Intelligence Suite Workshop Video Tutorial Series (3/5): Azure Data Factory

Machine Learning, predictive analytics, web services and all the rest to make it happen are really about one thing. And that is to acquire, process and act on data. For the workshop, this is done with a Data Factory pipeline configured to automatically upload a dataset to the storage account of a Spark cluster where… Continue reading Microsoft Cortana Intelligence Suite Workshop Video Tutorial Series (3/5): Azure Data Factory