Tagged with predictive analytics

The PMML Revolution: Predictive analytics at the speed of business

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, … Continue reading →

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 … Continue reading →

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 … Continue reading →

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. … Continue reading →

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 … Continue reading →

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 … Continue reading →

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 … Continue reading →

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.  … Continue reading →

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 … Continue reading →

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 … Continue reading →

R still the preferred tool of predictive modelers competing at Kaggle

As reported on the Kaggle blog No Free Hunch, R remains the preferred tool for data scientists seeking to win the prizes in the predictive modeling competitions: More than 30% of Kaggle competitors report using R for their analysis, up from 22% a year ago. R's flexibility and the breadth of … Continue reading →

ACM Data Mining Camp 2011: Report

(By Joseph Rickert.) In San Jose topics like big data, map reduce, predictive models, mobile analytics and crowdsourcing draw a crowd even on a Saturday. So it turned out that the ACM data Mining Camp and "un-conference" was a very "happening" way to spend a Saturday. Over 500 … Continue reading →

Big Analytics: Closing the "clue gap" with Big Data

There's been an growing discussion over the past couple of years on the topic of Big Data: how to deal with the situation when you have more data than can be conveniently managed and analyzed by traditional software tools. But Big Data has little intrinsic value in its own right: its value is … Continue reading →

How Google uses R to make online advertising more effective

At JSM 2011 today, three Google employees (amongst the more than 20 Google delegates there) gave a little insight into how statistical analysis with R yields better results for companies using Google's various advertising products. Bill Heavlin from Google kicked off the session with a talk about … Continue reading →

Fast logistic regression on Big Data with commodity hardware? No problem.

You might think that doing advanced statistical analysis on Big Data is out of reach for those of us without access to expensive hardware and software. For example, back in April SAS was proud to demonstrate being able to run logistic regression on a billion records (and "just a few" … Continue reading →