This guest post is by Alex Guazzelli, VP of Analytics at Zementis Inc. -- ed. PMML, the Predictive Model Markup Language, is the de facto standard to represent predictive analytics and data mining models. With PMML, it is extremely easy to move a predictive solution from one system to another, since it avoids proprietary issues and incompatibilities. Companies around...
Posts Tagged ‘ predictive analytics ’
Cheat sheet for prediction and classification models in R
Ricky Ho has created a reference a 6-page PDF reference card on Big Data Machine Learning, with examples implemented in the R language. (A free registration to DZone Refcardz is required to download the PDF.) The examples cover: Predictive modeling overview (how to set up test and training sets in R) Linear regression (using lm) Logistic regression (using glm)...
Orbitz and the Macs: Signals, not segmentation
By now you've probably heard about the fact that Orbitz users accessing the site via Macs are seeing more expensive hotel options when they search. But it seems worth clearing up a couple of fallacies. First, it's not as if the same hotel room is being offered at a higher prices to Mac users. (So no, using Windows to...
R Tops Data Mining Software Poll
For the past 12 years, KDNuggets has conducted an annual poll asking "What analytics/data mining software you used in the past 12 months for a real project (not just evaluation)". In this year's poll, R was the top-ranked data mining solution, selected by 30.7% of poll respondents. Microsoft Excel was second, at 29.8%. Rapidminer, which took the #1 spot...
R is to SAS as Java is to COBOL
An interview with Revolution Analytics CEO Dave Rich was published this week by BeyeNetwork. During the interview, Dace was asked about how the statistical modeling platforms have changed over the decades: People have been doing statistical modeling and predictive analytics for 50 years now, SAS and SPSS have been around since the early ‘70s. What’s different now -- what’s...
Simple tools for building a recommendation engine
By Joseph Rickert Revolution’s resident economist, Saar Golde, is very fond of saying that “90% of what you might from a recommendation engine can be achieved with simple techniques”. To illustrate this point (without doing a lot of work), we downloaded the million row movie dataset from www.grouplens.org with the idea of just taking the first obvious exploratory step:...
Marketing optimization with LityxIQ
Marketing is one of the pioneering domains when it comes to applications of predictive analytics to Big Data. (For example, how Target used statistical modeling to predict demographic attribues of customers, like pregnancy, to target coupons.) To get such powerful insights into the hands of marketers, DC-area company LityxIQ provides a cloud-based solution: LityxIQ, "an integrated hosted analytics platform...
Predicting Marketing Campaign with R
In my last blog I created a mechanism to fetch data from Salesforce using rJava and SOQL. In this blog I am going to use that mechanism to fetch ad campaign data from salesforce and predict future ad campaign sales using R Let us assume that Salesforce has campaign data for last eight quarters. This 
Big-data Naive Bayes and Classification Trees with R and Netezza
The IBM Netezza analytics appliances combine high-capacity storage for Big Data with a massively-parallel processing platform for high-performance computing. With the addition of Revolution R Enterprise for IBM Netezza, you can use the power of the R language to build predictive models on Big Data. In the demonstration below, Revolution Analytics' Derek Norton analyzes loan approval data stored on...
Big Data Analytics to Revolutionize Services
Revolution Analytics' CEO Dave Rich was interviewed by Wikibon's David Vellante and SiliconAngle's John Furrier at the Strata 2012 conference last week. Given Dave's background at Accenture Analytics, the conversation naturally turned to impact of predictive analytics and R on business services. (See the video after the jump, below.) Bret Latmore of SiliconANGLE provides highlights of the interview, including...
Zero Inflated Models and Generalized Linear Mixed Models with R.
Zuur, Saveliev, Ieno (2012).
