Interaction Design for Data Exploration Visualizations capable of launching detail views can add value to a data analyst’s user experience. Programming in this kind of interaction automates the creation of complementary charts and increases ease of exploration by linking varied views of the data in a logical way. This tutorial offers a quick example of
At Red Oak Strategic, we utilize a number of machine learning, AI and predictive analytics libraries, but one of our favorites is h2o. Not only is it open-source, powerful and scalable, but there is a great community of fellow h2o users that have helped over the years, not to mention the staff leadership at the
Introduction This post will demonstrate how to use machine learning to forecast time series data. The data set is from a recent Kaggle competition to predict retail sales. You will learn how to: Build a machine learning model to forecast time series data (data cleansing, feature engineering and modeling) Perform feature engineering to build
Frequently, we encounter projects that require the combined use of Python, Microsoft Excel and some external databases that can only be accessed via Excel, or use cases that require the end product to be output to that format. Excel is still used as a key program for the vast majority of businesses and we are
Our team recently designed a dashboard using R Shiny Leaflet allowing users to select many locations at one go on an interactive map. We created the map using the package [crayon-5c1316a585f39827891572-i/], which enables users to draw shapes on R Shiny Leaflet maps. When combined with the package [crayon-5c1316a585f44013576874-i/] and a function called [crayon-5c1316a585f47151580915-i/], the [crayon-5c1316a585f4a028093680-i/] drawing tool can
