We’re into the fourth day of Business Science Demo Week. We have a really cool one in store today: tibbletime, which uses a new tbl_time class that is time-aware!! For those that may have missed it, every day this week we are demo-ing an R package: tidyquant (Monday), timetk (Tuesday), sweep (Wednesday), tibbletime (Thursday) and h2o (Friday)! That’s five packages in five days! We’ll give you intel on what you need to know about these packages to go from zero to hero. Let’s take tibbletime for a spin!

Tonight at 7PM EST, we will be giving a LIVE #DataTalk on Using Machine Learning to Predict Employee Turnover. Employee turnover (attrition) is a major cost to an organization, and predicting turnover is at the forefront of needs of Human Resources (HR) in many organizations. Until now the mainstream approach has been to use logistic regression or survival curves to model employee attrition. However, with advancements in machine learning (ML), we can now get both better predictive performance and better explanations of what critical features are linked to employee attrition. We used two cutting edge techniques: the h2o package’s new FREE automatic machine learning algorithm, h2o.automl(), to develop a predictive model that is in the same ballpark as commercial products in terms of ML accuracy. Then we used the new lime package that enables breakdown of complex, black-box machine learning models into variable importance plots. The talk will cover HR Analytics and how we used R, H2O, and LIME to predict employee turnover.

We’re into the third day of Business Science Demo Week. Hopefully by now you’re getting a taste of some interesting and useful packages. For those that may have missed it, every day this week we are demo-ing an R package: tidyquant (Monday), timetk (Tuesday), sweep (Wednesday), tibbletime (Thursday) and h2o (Friday)! That’s five packages in five days! We’ll give you intel on what you need to know about these packages to go from zero to hero. Today is sweep, which has broom-style tidiers for forecasting. Let’s get going!

We’re into the second day of Business Science Demo Week. What’s demo week? Every day this week we are demoing an R package: tidyquant (Monday), timetk (Tuesday), sweep (Wednesday), tibbletime (Thursday) and h2o (Friday)! That’s five packages in five days! We’ll give you intel on what you need to know about these packages to go from zero to hero. Second up is timetk, your toolkit for time series in R. Here we go!

We’ve got an exciting week ahead of us at Business Science: we’re launching our first ever Business Science Demo Week. Every day this week we are demoing an R package: tidyquant (Monday), timetk (Tuesday), sweep (Wednesday), tibbletime (Thursday) and h2o (Friday)! That’s five packages in five days! We’ll give you intel on what you need to know about these packages to go from zero to hero. First up is tidyquant, our flagship package that’s useful for financial and time series analysis. Here we go!

Predictive sales analytics to predict product backorders can increase sales and customer satisfaction. Using a Kaggle dataset, we use H2O AutoML predict backorders.

Today we are introducing tibbletime v0.0.2, and we’ve got a ton of new features in store for you. We have functions for converting to flexible time periods with the ~period formula~ and making/calculating custom rolling functions with rollify() (plus a bunch more new functionality!). We’ll take the new functionality for a spin with some weather data (from the weatherData package). However, the new tools make tibbletime useful in a number of broad applications such as forecasting, financial analysis, business analysis and more! We truly view tibbletime as the next phase of time series analysis in the tidyverse. If you like what we do, please connect with us on social media to stay up on the latest Business Science news, events and information!

Predict employee turnover using the H2O machine learning and Lime.