# Using SASÂ® Enterprise MinerTM to Predict the Number of Rings on

```SAS Analytics Day
Using SAS® Enterprise MinerTM to Predict the Number of Rings on Abalone Shells
Gangarajula Ganesh, SAS & OSU Data Mining Student, Oklahoma State University
Seetharama Yogananda Domlur, Manager II – Customer Analytics, Walmart
4/28/2015
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Introduction – What is an Abalone? Why Analyze it?
 A type of sea snail with shell covering.
 Layered rings form the shell, which grow with age
 Shell structure under research to build strong body armor.
 Traditional approach of counting rings is tedious and time consuming.
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Data Description
 Data Source: UC Irvine Database.
 4177 Observations, 10 independent variables
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Variable Name
Description
Length
Length of the shell
Diameter
Diameter of the shell perpendicular to length
Height
Height of the shell
Shell Weight
Shell weight after being dried
Shucked Weight
Weight of the meat
Viscera Weight
Gut weight after bleeding
Whole Weight
Whole Abalone weight
Male
Male indicator
Female
Female indicator
Infant
Infant indicator
Rings
Number of rings on the shell
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Data Preparation
 Data Partitioning
• Training – 70%
• Validation – 30%
 Summary Statistics
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Modeling
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Results
 Best selected model: Neural Network  Best explainable model: Stepwise Linear Regression
 Selection Criteria: Average Squared Error
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Model Results: Stepwise Linear Regression Model
Parameter Estimates:
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Conclusions
1. The number of rings increase by 10.78 units for a
2.
3.
4.
5.
unit increase in Diameter.
The number of rings increase by 9.41 units for a
unit increase in Height.
The number of rings increase by 19.27 units for a
unit increase in Shell Weight.
The number of rings decrease by 0.63 units for a
unit increase in Infant class, signifying an infant.
The number of rings increase by 0.23 units for a
unit increase in Gender‐Female class.
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Acknowledgement
 We thank Dr. Goutam Chakraborty, Ralph A. and Peggy A. Brenneman Professor of Marketing & Founder of SAS and OSU Data Mining Certificate Program‐Oklahoma State University for all his support and guidance throughout the project.
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References
 http://www.aps.org/publications/capitolhillqu
arterly/201105/seasnails.cfm
 http://archive.ics.uci.edu/ml/datasets/Abalon
e [Bache, K. & Lichman, M. (2013). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA]
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Disclaimer
 We have analysed this topic using standard data mining and statistical techniques and we do not claim to have any kind of expertise in understanding the biology of Abalone Shells.
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Thank You!!
Ganesh Gangarajula
[email protected]
Yogananda Domlur Seetharama
[email protected]
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