Coverart for item
The Resource Applied analytics through case studies using SAS and R : implementing predictive models and machine learning techniques, Deepti Gupta

Applied analytics through case studies using SAS and R : implementing predictive models and machine learning techniques, Deepti Gupta

Label
Applied analytics through case studies using SAS and R : implementing predictive models and machine learning techniques
Title
Applied analytics through case studies using SAS and R
Title remainder
implementing predictive models and machine learning techniques
Statement of responsibility
Deepti Gupta
Creator
Author
Subject
Genre
Language
eng
Member of
Cataloging source
N$T
http://library.link/vocab/creatorName
Gupta, Deepti
Dewey number
338.7
Index
index present
LC call number
HB3730
Literary form
non fiction
Nature of contents
  • dictionaries
  • bibliography
http://library.link/vocab/subjectName
  • Business enterprises
  • Machine learning
  • R (Computer program language)
Label
Applied analytics through case studies using SAS and R : implementing predictive models and machine learning techniques, Deepti Gupta
Instantiates
Publication
Copyright
Antecedent source
unknown
Bibliography note
Includes bibliographical references and index
Carrier category
online resource
Carrier category code
cr
Carrier MARC source
rdacarrier
Color
multicolored
Content category
text
Content type code
txt
Content type MARC source
rdacontent
Contents
  • Intro; Table of Contents; About the Author; About the Contributor; About the Technical Reviewer; Acknowledgments; Introduction; Chapter 1: Data Analytics and Its Application in Various Industries; What Is Data Analytics?; Data Collection; Data Preparation; Data Analysis; Model Building; Results; Put into Use; Types of Analytics; Understanding Data and Its Types; What Is Big Data Analytics?; Big Data Analytics Challenges; Data Analytics and Big Data Tools; Role of Analytics in Various Industries; Who Are Analytical Competitors?; Key Models and Their Applications in Various Industries; Summary
  • Predictive Value Validation in Logistic Regression ModelLogistic Regression Model Using R; About Data; Performing Data Exploration; Model Building and Interpretation of Full Data; Model Building and Interpretation of Training and Testing Data; Predictive Value Validation; Logistic Regression Model Using SAS; Model Building and Interpretation of Full Data; Summary; References; Chapter 3: Retail Case Study; Supply Chain in the Retail Industry; Types of Retail Stores; Role of Analytics in the Retail Sector; Customer Engagement; Supply Chain Optimization; Price Optimization
  • Space Optimization and Assortment PlanningCase Study: Sales Forecasting for Gen Retailers with SARIMA Model; Overview of ARIMA Model; AutoRegressive Model; Moving Average Model; AutoRegressive Moving Average Model; The Integrated Model; Three Steps of ARIMA Modeling; Identification Stage; Estimation and Diagnostic Checking Stage; Forecasting Stage; Seasonal ARIMA Models or SARIMA; Evaluating Predictive Accuracy of Time Series Model; Seasonal ARIMA Model Using R; About Data; Performing Data Exploration for Time Series Data; Seasonal ARIMA Model Using SAS; Summary; References
  • Chapter 4: Telecommunication Case StudyTypes of Telecommunications Networks; Role of Analytics in the Telecommunications Industry; Predicting Customer Churn; Network Analysis and Optimization; Fraud Detection and Prevention; Price Optimization; Case Study: Predicting Customer Churn with Decision Tree Model; Advantages and Limitations of the Decision Tree; Handling Missing Values in the Decision Tree; Handling Model Overfitting in Decision Tree; Prepruning; Postpruning; How the Decision Tree Works; Measures of Choosing the Best Split Criteria in Decision Tree; Decision Tree Model Using R
Dimensions
unknown
Extent
1 online resource.
File format
unknown
Form of item
online
Isbn
9781484235249
Level of compression
unknown
Media category
computer
Media MARC source
rdamedia
Media type code
c
Quality assurance targets
not applicable
Reformatting quality
unknown
Sound
unknown sound
Specific material designation
remote
System control number
  • on1047959881
  • (OCoLC)1047959881
Label
Applied analytics through case studies using SAS and R : implementing predictive models and machine learning techniques, Deepti Gupta
Publication
Copyright
Antecedent source
unknown
Bibliography note
Includes bibliographical references and index
Carrier category
online resource
Carrier category code
cr
Carrier MARC source
rdacarrier
Color
multicolored
Content category
text
Content type code
txt
Content type MARC source
rdacontent
Contents
  • Intro; Table of Contents; About the Author; About the Contributor; About the Technical Reviewer; Acknowledgments; Introduction; Chapter 1: Data Analytics and Its Application in Various Industries; What Is Data Analytics?; Data Collection; Data Preparation; Data Analysis; Model Building; Results; Put into Use; Types of Analytics; Understanding Data and Its Types; What Is Big Data Analytics?; Big Data Analytics Challenges; Data Analytics and Big Data Tools; Role of Analytics in Various Industries; Who Are Analytical Competitors?; Key Models and Their Applications in Various Industries; Summary
  • Predictive Value Validation in Logistic Regression ModelLogistic Regression Model Using R; About Data; Performing Data Exploration; Model Building and Interpretation of Full Data; Model Building and Interpretation of Training and Testing Data; Predictive Value Validation; Logistic Regression Model Using SAS; Model Building and Interpretation of Full Data; Summary; References; Chapter 3: Retail Case Study; Supply Chain in the Retail Industry; Types of Retail Stores; Role of Analytics in the Retail Sector; Customer Engagement; Supply Chain Optimization; Price Optimization
  • Space Optimization and Assortment PlanningCase Study: Sales Forecasting for Gen Retailers with SARIMA Model; Overview of ARIMA Model; AutoRegressive Model; Moving Average Model; AutoRegressive Moving Average Model; The Integrated Model; Three Steps of ARIMA Modeling; Identification Stage; Estimation and Diagnostic Checking Stage; Forecasting Stage; Seasonal ARIMA Models or SARIMA; Evaluating Predictive Accuracy of Time Series Model; Seasonal ARIMA Model Using R; About Data; Performing Data Exploration for Time Series Data; Seasonal ARIMA Model Using SAS; Summary; References
  • Chapter 4: Telecommunication Case StudyTypes of Telecommunications Networks; Role of Analytics in the Telecommunications Industry; Predicting Customer Churn; Network Analysis and Optimization; Fraud Detection and Prevention; Price Optimization; Case Study: Predicting Customer Churn with Decision Tree Model; Advantages and Limitations of the Decision Tree; Handling Missing Values in the Decision Tree; Handling Model Overfitting in Decision Tree; Prepruning; Postpruning; How the Decision Tree Works; Measures of Choosing the Best Split Criteria in Decision Tree; Decision Tree Model Using R
Dimensions
unknown
Extent
1 online resource.
File format
unknown
Form of item
online
Isbn
9781484235249
Level of compression
unknown
Media category
computer
Media MARC source
rdamedia
Media type code
c
Quality assurance targets
not applicable
Reformatting quality
unknown
Sound
unknown sound
Specific material designation
remote
System control number
  • on1047959881
  • (OCoLC)1047959881

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