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DATA PREPARATION TECHNIQUES

FOR PREDICTIVE ANALYTICS IN

SAS ENTERPRISE GUIDE

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SAS

ENTERPRISE

GUIDE

DATA PREPARATION TECHNIQUES FOR

ANALYTICS

Data & Data Format Needed for

Predictive Modeling Create a Y or Dependent Variable

Create Model Input Variables

Replace Missing Values

Assess Normality

Transform Variables in Order to MeetAssumptions

Run Linear and Logistic Regression

Q&A

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SCENARIO

Company sells Outdoor and Sport

Want to test a new marketing cam

Need to compile a data table with

so we can build a predictive mode

likely to respond.

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CUSTOMER DATA

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PRODUCT ORDER

DETAIL DATATRANSACTIONAL

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PREDICTIVE MODEL

DEVELOPMENT

DATA

Modeling Data Set Data Warehouse

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MAIN TYPES OF

DATA MARTSDATA FOR MODELING

One-Row-per-

Subject DataMart

Multiple-Row-per-Subject DataMart

LongitudinalData Mart

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THE ONE-ROW-PER-

SUBJECT DATA

MART

Required by many statistical methods

Regression Analysis, Neural Networks, Decision Trees, Surviva

Cluster analysis,

Most prominent data mart structure in data mining

Event prediction (Churn, Fraud, Delinquency, Response, )

Value prediction (Purchase Size, Claim Amount, )

Segmentation (Clustering, )

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OUR DESTINATION ONE ROW PER CUSTOMER

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THE ONE-ROW-PER-

SUBJECT

PARADIGM

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TARGET SAMPLE

Target Sample Data Warehouse

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CREATE TARGET

(Y) VARIABLE

Create a variable indicating who

Purchased in the last 2 years (i.e.

January 1, 2012 December 31,

2013)

New Variable named PURCHASED1=Yes

0=No

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DEPENDENT

VARIABLE

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CREATE DEPENDENT (Y) VARIABLE DEMO

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MODEL INPUTS

Data WarehouseModel Inputs

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ENTERPRISE

GUIDEQUERY BUILDER: COMPUTED COLUMNS

COMPUTED

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COMPUTED

COLUMNS SUMMARIZED IS ONE EXAMPLE

COMPUTED

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COMPUTED

COLUMNS RECODED IS ANOTHER EXAMPLE

COMPUTED

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COMPUTED

COLUMNS ADVANCED EXPRESSION IS YET ANOTHER E

C G

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CREATE AGE

VARIABLECOMPUTED COLUMN

Calculate a customers age from their

using Advanced Expression and SAS

New Variable named AGE

YRDIF(t1.Customer_BirthDate,TODAY(),"ACT/

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CREATE CALCULATED INPUT (AGE)

MODEL

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O

DEVELOPMENT AND

TRANSACTION DATA

INPUT

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POSSIBILITIES:

TABULATIONS

CREATE SUMMARY

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CREATE SUMMARY

VARIABLESCOMPUTED COLUMNS

Calculate Summary Variables aboutpurchases

Total Amount Spent = TotalSpent

Total Number of Items Bought = TotalItems

Average Amount Spent = AvgSpent

Calculate New Variable for eachtransaction and take the Max

Longest Number of Days for a Delivery to

arrive = DaystoDeliver

CREATE SUMMARY

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CREATE SUMMARY

VARIABLESCOMPUTED COLUMNS

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CREATE SUMMARY INPUTS

CREATE

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INDICATOR

VARIABLES

Calculate Indicator Variables

Did the customer buy a product in a certaline?

Childrens, Clothes & Shoes, Sports

Calculate New Variable for each transa

Product Line and summarize Total_Re

Gives us the total spent for each product Product Line Description = ProductLineD

Total of Product Line for order = OrderTot

Distinct Count of Customer_ID = Indicato

CREATE

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CREATE

INDICATORS

CREATE CATEGORY

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CREATE CATEGORY

TOTALS

3 TASKS TO

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3 TASKS TO

TRANSPOSE DATA

1. Transpose Switch rows with

columns and columns with rows

2. Split Split one columns into

multiple columns

3. Stack Stack multiple columnsinto one column

SPLIT COLUMNS

THREE QUESTIONS

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SPLIT COLUMNS

TASKTHREE QUESTIONS

1. Which column is being split?

2. Which column identifies the valu

being split?3. Which column groups the data?

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CREATE INDICATOR INPUTSUSING RECODE, SUMMARY &

TRANSPOSE

Creating indicator variables in PROC SQL

REPLACING

http://www.nesug.org/Proceedings/nesug97/coders/eddlesto.pdfhttp://www.nesug.org/Proceedings/nesug97/coders/eddlesto.pdf

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MISSING VALUES

Enterprise Guide Query Builder Compu

Replace Values

SAS Code

PROC STDIZE - documentation

SAS/STAT PROC MI - documentation

Enterprise Miner Impute Node

Class variables count, default constant valu

tree, tree surrogate

Target variables count, default constant va Interval variables mean, median, midrange

tree, tree surrogate, mid-minimum spacing, Tu

Huber, Andrews Wave, default constant

What is Missing in SAS?

REPLACEMENT TWO OPTIONSPRO

http://support.sas.com/documentation/cdl/en/statug/63347/HTML/default/viewer.htm%23statug_stdize_sect004.htmhttp://support.sas.com/documentation/cdl/en/statug/63962/HTML/default/viewer.htm%23statug_mi_sect001.htmhttp://analytics.ncsu.edu/sesug/2003/PS08-Go.pdfhttp://analytics.ncsu.edu/sesug/2003/PS08-Go.pdfhttp://support.sas.com/documentation/cdl/en/statug/63962/HTML/default/viewer.htm%23statug_mi_sect001.htmhttp://support.sas.com/documentation/cdl/en/statug/63347/HTML/default/viewer.htm%23statug_stdize_sect004.htm

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REPLACEMENT TWO OPTIONS

PROC STDIZE

out =dat aprep. WhoPur chased

r eponl y mi ssi ng=0;r un;

PRO

PROC DATASETS l i b=wor k;MODI FY zer os;FORMAT _al l _;I NFORMAT _al l _;RUN;

or

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MISSING VALUE REPLACEMENT DEMO

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ASSESS NORMALITY

ASSESS

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NORMALITY

Tasks

Describe

DistributionAnalysis

Graphs

Histograms

Q-Q Plot

Kernel Density Plot

ASSESS

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NORMALITY

TasksDescribeDistribution

Analysis

4 Tests

Shapiro-Wilk

Kolmogorow-Smirnov (K-S)

Cramer-von Mises

Anderson-Darling

Testing Normality of Data using SAS

Guidos Guide to PROC Univariate: A tutorial for SAS

Users

http://www.lexjansen.com/pharmasug/2004/posters/po04.pdfhttp://www.nesug.org/Proceedings/nesug09/sa/sa07.pdfhttp://www.nesug.org/Proceedings/nesug09/sa/sa07.pdfhttp://www.nesug.org/Proceedings/nesug09/sa/sa07.pdfhttp://www.lexjansen.com/pharmasug/2004/posters/po04.pdf

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ASSESS NORMALITY DEMO

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TRANSFORM VARIABLES

TRANSFORMATIONS

FOR NORMALITY

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FOR NORMALITY

Log

Square Root

Cube Root

Reciprocal

Square Transformation

Many more

TRANSFORMING

VARIABLES

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VARIABLES

TotalSpent Log Transformation

Age Recode to categorical

Transforming Variables for Normality and Linearity

Before Logistic Modeling A Toolkit for Identifying an

Transforming Relevant Predictors

COMPUTED

COLUMNSADVANCED EXPRESSION

http://www.nesug.org/proceedings/nesug05/an/an7.pdfhttp://www.lexjansen.com/mwsug/2011/stats/MWSUG-2011-SA03.pdfhttp://www.lexjansen.com/mwsug/2011/stats/MWSUG-2011-SA03.pdfhttp://www.lexjansen.com/mwsug/2011/stats/MWSUG-2011-SA03.pdfhttp://www.lexjansen.com/mwsug/2011/stats/MWSUG-2011-SA03.pdfhttp://www.nesug.org/proceedings/nesug05/an/an7.pdf

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COLUMNS

COMPUTED

COLUMNSRECODED

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COLUMNS

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TRANSFORM VARIABLES DEMO

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RESOURCES

DATA PREPARATIONFOR ANALYTICS

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USING SAS

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ADDITIONAL

SUPPORTENTERPRISE GUIDE TUTORIALS

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Ad Hoc Data Preparation for Analysis Usin

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Take a Fresh Look at SAS Enterprise Guid

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http://www.nesug.org/Proceedings/nesug09/ff/ff14.pdfhttp://www.nesug.org/Proceedings/nesug09/ff/ff14.pdfhttp://www.nesug.org/Proceedings/nesug09/ff/ff14.pdfhttp://www2.sas.com/proceedings/sugi29/117-29.pdfhttp://www2.sas.com/proceedings/sugi29/117-29.pdfhttp://www2.sas.com/proceedings/sugi29/117-29.pdfhttp://www2.sas.com/proceedings/sugi29/117-29.pdfhttp://www2.sas.com/proceedings/sugi22/STATS/PAPER267.PDFhttp://www2.sas.com/proceedings/sugi22/STATS/PAPER267.PDFhttp://www2.sas.com/proceedings/sugi22/STATS/PAPER267.PDFhttp://www.scsug.org/SCSUGProceedings/2011/schacherer2/Take%20a%20Fresh%20Look%20at%20SASR%20Enterprise%20GuideR%20-%20From%20point-and-click%20ad%20hocs%20to%20robust%20enterprise%20solutions.pdfhttp://www.scsug.org/SCSUGProceedings/2011/schacherer2/Take%20a%20Fresh%20Look%20at%20SASR%20Enterprise%20GuideR%20-%20From%20point-and-click%20ad%20hocs%20to%20robust%20enterprise%20solutions.pdfhttp://www.scsug.org/SCSUGProceedings/2011/schacherer2/Take%20a%20Fresh%20Look%20at%20SASR%20Enterprise%20GuideR%20-%20From%20point-and-click%20ad%20hocs%20to%20robust%20enterprise%20solutions.pdfhttp://www2.sas.com/proceedings/sugi28/009-28.pdfhttp://www2.sas.com/proceedings/sugi28/009-28.pdfhttp://www.youtube.com/watch?v=4VAIhWUYrWo&feature=relmfuhttp://www.youtube.com/watch?v=4VAIhWUYrWo&feature=relmfuhttp://www.youtube.com/watch?v=4VAIhWUYrWo&feature=relmfuhttp://www2.sas.com/proceedings/sugi28/009-28.pdfhttp://www.scsug.org/SCSUGProceedings/2011/schacherer2/Take%20a%20Fresh%20Look%20at%20SASR%20Enterprise%20GuideR%20-%20From%20point-and-click%20ad%20hocs%20to%20robust%20enterprise%20solutions.pdfhttp://www2.sas.com/proceedings/sugi22/STATS/PAPER267.PDFhttp://www2.sas.com/proceedings/sugi29/117-29.pdfhttp://www.nesug.org/Proceedings/nesug09/ff/ff14.pdf

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Enterprise Guide 1 : Query and

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Data Preparation for Data Mining

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