Advancements in machine learning and artificial intelligence (AI) opens new doors for businesses to make data-informed decisions, and if your business does not take advantage of the growing industry of data analytics, your competitors will steamroll ahead.
When it comes to finding out who your best customers are, the old RFM matrix principle is at work again. RFM stands for Recency, Frequency, and Monetary. It is a customer segmentation technique that uses past purchase behavior to divide customers into groups.
At Instacart, our goal is to make grocery delivery accessible to everyone. In the last 9 months, we grew tremendously from a handful of established markets to over 100+ markets in the US. Such rapid expansion brings a lot of growth potential but also adds more unpredictability to our demand. Offering every customer same day delivery, while keeping our shoppers busy becomes a very hard problem. This post is about how we use Monte Carlo simulations to balance supply and demand in a rapidly growing, high-variance marketplace.
Today we are excited to announce the availability of RStudio Professional Drivers. There are, of course, many ways to connect to Databases using R. RStudio Professional Drivers are specifically intended for use with our professional products, including RStudio Server Pro, Shiny Server Pro, and RStudio Connect. These data connectors combined with enhancements to dplyr, the odbc package, and the RStudio IDE provide a comprehensive suite of tools for accessing and analyzing data with your enterprise systems.
Social media and machine learning concepts are transforming self-service data prep into a collaborative data marketplace.
In this article we focus on the personalization aspect of model building and explain the modeling principle as well as how to implement Photon-ML so that it can scale to hundreds of millions of users.
One might think that the function of a fire alarm is to provide you with important evidence about a fire existing, allowing you to change your policy accordingly and exit the building. In the classic experiment by Latane and Darley in 1968, eight groups of three students each were asked to fill out a questionnaire in a room that shortly after began filling up with smoke. Five out of the eight groups didn’t react or report the smoke, even as it became dense enough to make them start coughing. Subsequent manipulations showed that a lone student will respond 75% of the time; while a student accompanied by two actors told to feign apathy will respond only 10% of the time. This and other experiments seemed to pin down that what’s happening is pluralistic ignorance. We don’t want to look panicky by being afraid of what isn’t an emergency, so we try to look calm while glancing out of the corners of our eyes to see how others are reacting, but of course they are also trying to look calm. …
This weekend, I decided it was time: I was going to update my Python environment and get Keras and Tensorflow installed so I could start doing tutorials (particularly for deep learning) using R. Although I used to be a systems administrator (about 20 years ago), I don’t do much installing or configuring so I guess that’s why I’ve put this task off for so long. And it wasn’t unwarranted: it took me the whole weekend to get the install working. Here are the steps I used to get things running on Windows 10, leveraging clues in about 15 different online resources — and yes (I found out the hard way), the order of operations is very important. I do not claim to have nailed the order of operations here, but definitely one that works.
There have been several blog posts going around about why one would use Docker with R. In this post I’ll try to add a DevOps point of view and explain how containerizing R is used in the context of the OpenCPU system for building and deploying R servers.
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!
In this post we will explore how to make SVG tables in R using plotly. The tables are visually appealing and can be modified on the fly using simple drag and drop. Make sure you install the latest version of Plotly i.e. v 188.8.131.52 from Github using devtools::install_github(‘ropensci/plotly)