A research team hailing from Rensselaer Polytechnic Institute and Raytheon BBN Technologies used features from article content to create a predictive model for gauging a given community’s interest in news articles. For the most part, past research in this realm has attempted to forecast general popularity in lieu of niche... Read more

In business, the value of being able to accurately predict outcomes – asset failures, market fluctuations, or customer churn, for example – can scarcely be understated. The growth of data analytics in business in recent years is largely attributable to growing demand for predictive modeling. Today, artificial intelligence is making... Read more

This is the second post in a two-part series that discusses healthcare predictive and propensity modeling and selecting the optimal analytics partner to support your growth and engagement efforts. The first post in this series shares five best practices in healthcare propensity modeling. In our last post, we talked about big data,... Read more

A wise philosopher (or comedian) once said, “Even a broken clock is right twice a day.” That same statement might also apply to some predictive models. Since prediction is about the future (usually), then random chance (like broken clockwork) may allow our model to be right occasionally (just by accident). The important... Read more

I’m an actuary. That means I use numbers to try to understand human behavior, manage risk, and evaluate the likelihood that a particular thing will happen in the future. Most people associate my work with green eyeshades and the morbid business of predicting how long someone is likely to live.... Read more

Predictive Modeling Workshop from odsc The workshop is an overview of creating predictive models using R. An example data set will be used to demonstrate a typical workflow: data splitting, pre-processing, model tuning and evaluation. Several R packages will be shown along with the caret package which provides a unified... Read more

People who use predictive analytics models aim to look at current and past data and use it to pinpoint the likelihood of future outcomes. Here are six common types of predictive analytics models and potential reasons to use them.  1. Decision Tree Model The decision tree model has a structure... Read more

Predictive analytics works in the real world without us understanding exactly how. We turn on our cell phones and expect them to work without understanding the fundamentals. We push the gas pedal and expect the car to go. Even something as simple as a pencil may not be common knowledge... Read more

Machine learning models are powerful tools that do well in their purpose of prediction. In many business applications, the power of these models is quite beneficial. With any application of a machine learning model, the process to choosing which model involves determining the model that performs best across a given... Read more

This was originally posted on the Silicon Valley Data Science blog. In a previous post, we introduced an example of an IoT predictive maintenance problem. We framed the problem as one of estimating the remaining useful life (RUL) of in-service equipment, given some past operational history and historical run-to-failure data. Reading that post... Read more