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Training
Survival Data Mining: Modeling Customer Event Histories
Customer databases contain histories of vital events such as the
acquisition and cancellation of products and services. The historical data
is used to build predictive models for customer retention, cross selling,
and other database marketing endeavors. The temporal nature of the target
events requires specialized statistical methods designed for censored
duration data -- survival analysis.
This course will cover the application of survival analysis to
business problems. The topics addressed will include discrete-time hazard
modeling, time-dependent covariates, and competing risks. Most of the
examples will use a combination of BASE and STAT programming. In addition,
the use of neural networks for hazard modeling will be introduced using
Enterprise Miner software.
Prerequisites
Before attending this course, you should have completed the Predictive
Modeling Using Logistic Regression or the Neural Network
Modeling course or have equivalent experience.
| Duration: 2.5 days
| CEUs: 1.5
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What participants say about the M-series:
"The educational content, exchange of ideas, and intellectual environment
I found at the conference exceeded my expectations and confirmed SAS'
place as the premier data mining conference in the world."
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"Right time. Right place. Right content."
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"This was a superb environment - one of the smartest conference venues I
have experienced (and I have experienced a lot). The talks went into
greater depth than the talks at many such meetings. Many of the talks were
particularly valuable in shedding light on different application areas of
data mining."
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