Data Preparation for Data Mining Using SAS (Google eBook)
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.
* A complete framework for the data preparation process, including implementation details for each step.
* The complete SAS implementation code, which is readily usable by professional analysts and data miners.
* A unique and comprehensive approach for the treatment of missing values, optimal binning, and cardinality reduction.
* Assumes minimal proficiency in SAS and includes a quick-start chapter on writing SAS macros.
* CD includes dozens of SAS macros plus the sample data and the program for the book's case study.
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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
&IDVar &MissPat &TypeVar &var &Xvar algorithms binary binning calculate call symput categorical variables cluster contingency table continuous variables create table CustID data _null_ Data &DSout data mining data preparation data warehouse Database decision tree defined delete dependent variable Description DSin Input distribution DSin Input dataset DSout DSVarMap eigenvalues example extract factor analysis finish the macro frequencies function GiniRatio Header i=1 to &Nvars imputed values left(_N_ 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 ratio regression model rollup sample SAS macro scoring view set &DSin split temp_cats temp_freqs Temp_TERM transactions transformation variable name variance VarList VarName
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