Sales, customer service, supply chain and logistics, manufacturing… no matter which department you’re in, you more than likely care about backorders. Backorders are products that are temporarily out of stock, but a customer is permitted to place an order against future inventory. Back orders are both good and bad: Strong demand can drive back orders, but so can suboptimal planning. The problem is when a product is not immediately available, customers may not have the luxury or patience to wait. This translates into lost sales and low customer satisfaction. The good news is that machine learning (ML) can be used to identify products at risk of backorders. In this article we use the new H2O automated ML algorithm to implement Kaggle-quality predictions on the Kaggle dataset, “Can You Predict Product Backorders?”. This is an advanced tutorial, which can be difficult for learners. We have good news, see our announcement below if you are interested in a machine learning course from Business Science. If you love this tutorial, please connect with us on social media to stay up on the latest Business Science news, events and information! Good luck and happy analyzing!
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!
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. In this post, we’ll use two cutting edge techniques. First, we’ll use 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’ll use the new
lime package that enables breakdown of complex, black-box machine learning models into variable importance plots. We can’t stress how excited we are to share this post because it’s a much needed step towards machine learning in business applications!!! Enjoy.
We are very excited to announce the initial release of our newest R package,
tibbletime. As evident from the name,
tibbletime is built on top of the
tibble package (and more generally on top of the
tidyverse) with the main
purpose of being able to create time-aware tibbles through a one-time
specification of an “index” column (a column containing timestamp information). There are a ton of useful time functions that we can now use such as
time_collapse(). We’ll walk through the basics in this post.
We’re excited to announce the
alphavantager package, a lightweight R interface to the Alpha Vantage API! Alpha Vantage is a FREE API for retreiving real-time and historical financial data. It’s very easy to use, and, with the recent glitch with the Yahoo Finance API, Alpha Vantage is a solid alternative for retrieving financial data for FREE! It’s definitely worth checking out if you are interested in financial analysis. We’ll go through the
alphavantager R interface in this post to show you how easy it is to get real-time and historical financial data. In the near future, we have plans to incorporate the
tidyquant to enable scaling from one equity to many.
In the fourth part in a series on Tidy Time Series Analysis, we’ll investigate lags and autocorrelation, which are useful in understanding seasonality and form the basis for autoregressive forecast models such as AR, ARMA, ARIMA, SARIMA (basically any forecast model with “AR” in the acronym). We’ll use the
tidyquant package along with our tidyverse downloads data obtained from
cranlogs. The focus of this post is using
lag.xts(), a function capable of returning multiple lags from a xts object, to investigate autocorrelation in lags among the daily tidyverse package downloads. When using
tq_mutate() we can scale to multiple groups (different tidyverse packages in our case). If you like what you read, please follow us on social media to stay up on the latest Business Science news, events and information! As always, we are interested in both expanding our network of data scientists and seeking new clients interested in applying data science to business and finance. If interested, contact us.
In the third part in a series on Tidy Time Series Analysis, we’ll use the
runCor function from
TTR to investigate rolling (dynamic) correlations. We’ll again use
tidyquant to investigate CRAN downloads. This time we’ll also get some help from the
corrr package to investigate correlations over specific timespans, and the
cowplot package for multi-plot visualizations. We’ll end by reviewing the changes in rolling correlations to show how to detect events and shifts in trend. If you like what you read, please follow us on social media to stay up on the latest Business Science news, events and information! As always, we are interested in both expanding our network of data scientists and seeking new clients interested in applying data science to business and finance. If interested, contact us.
We have several announcements regarding Business Science R packages. First, as of this week the R package formerly known as
timekit has changed to
timetk for time series tool kit. There are a few “breaking” changes because of the name change, and this is discussed further below. Second, the
tidyquant packages have several improvements, which are discussed in detail below. Finally, don’t miss a beat on future news, events and information by following us on social media.