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 the IBM appliance. You'll see the R code used to:

Explore the raw data (with summary statistics and charts)

Prepare the data for statistical analysis, and create training and test sets

Create predictive models using classificiation trees and Naïve Bayes

Predict using the models, and evaluate model performance using confusion matrices

Note that while R code is being run on Derek's laptop, the raw data is never moved from the appliance, and the analytic computations take place "in-database" within the appliance itself (where the Revolution R Enterprise engine is also running on each parallel core).

This demo was included in the recent webinar, Turbo-Charge Your Analytics with IBM Netezza for which you can find slides and a replay at the link below.

Revolution Anlaytics Webinars: Turbo-Charge Your Analytics with IBM Netezza and Revolution R Enterprise: A Step-by-Step Approach for Acceleration and Innovation