Customer and business analytics : applied data mining for business decision making using R /

Detalhes bibliográficos
Autor principal: Putler, Daniel S.
Outros Autores: Krider, Robert E.
Formato: Livro
Idioma:English
Publicado em: Boca Raton, FL : CRC Press, 2015 [reprinted]
coleção:Chapman & Hall/CRC the R series
Assuntos:
Classic Catalogue: View this record in Classic Catalogue
LEADER 03268nam a2200421 a 4500
001 33624
003 BD-DhAAL
005 20211109100337.0
008 180808r20152012flua b 001 0 eng
999 |c 39399  |d 39399 
010 |a  2012008925 
016 7 |a 016039623  |2 Uk 
020 |a 9781466503960 (alk. paper) 
020 |a 1466503963 (alk. paper) 
035 |a (OCoLC)ocn756596227 
040 |a DLC  |b eng  |c DLC  |d YDX  |d BTCTA  |d UKMGB  |d YDXCP  |d OCLCO  |d BWX  |d DLC  |d BD-DhAAL 
042 |a pcc 
050 0 0 |a HF5415.126  |b .P88 2012 
082 0 0 |a 658.40302855133  |2 23 
100 1 |a Putler, Daniel S.  |9 27765 
245 1 0 |a Customer and business analytics :  |b applied data mining for business decision making using R /  |c Daniel S. Putler and Robert E. Krider. 
260 |a Boca Raton, FL :  |b CRC Press,  |c 2015 [reprinted]  
300 |a xxvi, 289 pages :  |b illustrations ;  |c 27 cm. 
490 1 |a Chapman & Hall/CRC the R series 
504 |a Includes bibliographical references (pages. 283-285) and index. 
505 |a I Purpose and Process Database Marketing and Data Mining Database Marketing Data Mining Linking Methods to Marketing Applications A Process Model for Data Mining-CRISP-DM History and Background The Basic Structure of CRISP-DMII Predictive Modeling Tools Basic Tools for Understanding Data Measurement Scales Software ToolsReading Data into R Tutorial Creating Simple Summary Statistics Tutorial Frequency Distributions and Histograms Tutorial Contingency Tables TutorialMultiple Linear Regression Jargon Clarification Graphical and Algebraic Representation of the Single Predictor ProblemMultiple RegressionSummary Data Visualization and Linear Regression TutorialLogistic RegressionA Graphical Illustration of the Problem The Generalized Linear Model Logistic Regression Details Logistic Regression TutorialLift Charts Constructing Lift Charts Using Lift Charts Lift Chart TutorialTree Models The Tree Algorithm Trees Models TutorialNeural Network Models The Biological Inspiration for Artificial Neural Networks Artificial Neural Networks as Predictive Models Neural Network Models TutorialPutting It All Together Stepwise Variable Selection The Rapid Model Development FrameworkApplying the Rapid Development Framework TutorialIII Grouping Methods Ward's Method of Cluster Analysis and Principal Components Summarizing Data Sets Ward's Method of Cluster Analysis Principal Components Ward's Method TutorialK-Centroids Partitioning Cluster Analysis How K-Centroid Clustering Works Cluster Types and the Nature of Customer Segments Methods to Assess Cluster Structure K-Centroids Clustering TutorialBibliography Index 
526 |a CSE 
541 |a Trim Education  |e 33624 
650 0 |a Database marketing  |x Software.  |9 27766 
650 0 |a Data mining.  |9 27767 
650 0 |a Decision making  |x Data processing.  |9 27768 
650 0 |a (Computer program language).  |9 27769 
650 0 |a Database management.  |9 27770 
650 0 |a Computer science.  |9 42452 
700 1 |a Krider, Robert E.  |9 27771 
852 |a Ayesha Abed Library  |c General Stacks 
942 |2 ddc  |c BK 
952 |0 0  |1 0  |2 ddc  |4 0  |6 658_403285513300000_PUT  |7 0  |9 64095  |a BRACUL  |b BRACUL  |c GEN  |d 2018-07-29  |e Karim International  |g 1306.60  |l 0  |o 658.4032855133 PUT  |p 3010033624  |r 2018-07-29  |t 1  |v 1306.60  |w 2018-07-29  |y BK