Data Preparation for Data Mining Using SAS
Are you a data mining analyst, who spends up to 80% of your time assuring data quality, then preparing that data for developing and deploying predictive models? And do you find lots of literature on data mining theory and concepts, but when it comes to practical advice on developing good mining views find little “how to information? And are you, like most analysts, preparing the data in SAS?
This book is intended to fill this gap as your source of practical recipes. It introduces a framework for the process of data preparation for data mining, and presents the detailed implementation of each step in SAS. In addition, business applications of data mining modeling require you to deal with a large number of variables, typically hundreds if not thousands. Therefore, the book devotes several chapters to the methods of data transformation and variable selection.
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A QUICK START
CHAPTER 5 DATA ACQUISITION AND INTEGRATION
CHAPTER 6 INTEGRITY CHECKS
CHAPTER 7 EXPLORATORY DATA ANALYSIS
CHAPTER 8 SAMPLING AND PARTITIONING
CHAPTER 13 ANALYSIS OF NOMINAL AND ORDINAL VARIABLES
CHAPTER 14 ANALYSIS OF CONTINUOUS VARIABLES
CHAPTER 15 PRINCIPAL COMPONENT ANALYSIS
CHAPTER 16 FACTOR ANALYSIS
CHAPTER 17 PREDICTIVE POWER AND VARIABLE REDUCTION II
CHAPTER 18 PUTTING IT ALL TOGETHER
APPENDIX LISTING OF SAS MACROS
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&IDVar &Nvars &var &WarX &Xvar algorithms binary binning calculate call symput categorical variables cluster contingency table continuous variables create table credit card CustID Data &DSout data mining data null_ data preparation data warehouse Database decision tree defined delete dependent variable Description DSin Input distribution DSin Input dataset DSout eigenvalues Equation example extract factor analysis finish the macro frequencies function Gini ratio Header imputed values likelihood function linear regression logistic regression loop macro variables mapping mend merge methods mining view missing pattern missing values nolist nominal variables number of records ordinal variables outliers output dataset p-value Parameter Description DSin Parameters of macro partitions population principal component proc datasets library=work proc sort proc sql noprint PROC UNIVARIATE procedures quit regression model rollup sample scoring view select count Set &DSin split temp_freqs Temp_TERM transaction transformation variable names variance VarList Xvar
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