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BL
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| Record Number
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851053
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| Main Entry
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Gupta, Deepti
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| Title & Author
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Applied analytics through case studies using SAS and R : : implementing predictive models and machine learning techniques /\ Deepti Gupta.
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| Publication Statement
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Boston, Massachusetts :: Apress,, [2018]
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, ©2018
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| Page. NO
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1 online resource (xx, 404 pages) :: illustrations
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| ISBN
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1484235258
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: 1484235266
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: 1484240464
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: 9781484235256
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: 9781484235263
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: 9781484240465
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148423524X
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9781484235249
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| Bibliographies/Indexes
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Includes bibliographical references and index.
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| Contents
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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
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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
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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
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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
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| Abstract
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Examine business problems and use a practical analytical approach to solve them by implementing predictive models and machine learning techniques using SAS and the R analytical language. This book is ideal for those who are well-versed in writing code and have a basic understanding of statistics, but have limited experience in implementing predictive models and machine learning techniques for analyzing real world data. The most challenging part of solving industrial business problems is the practical and hands-on knowledge of building and deploying advanced predictive models and machine learning algorithms. Applied Analytics through Case Studies Using SAS and R is your answer to solving these business problems by sharpening your analytical skills. What You'll Learn Understand analytics and basic data concepts Use an analytical approach to solve Industrial business problems Build predictive model with machine learning techniques Create and apply analytical strategies Who This Book Is For Data scientists, developers, statisticians, engineers, and research students with a great theoretical understanding of data and statistics who would like to enhance their skills by getting practical exposure in data modeling.
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| Subject
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Business enterprises-- Evaluation, Case studies.
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Machine learning.
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R (Computer program language)
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BUSINESS ECONOMICS-- Industries-- General.
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Business enterprises-- Evaluation.
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Business mathematics systems.
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Computer programming-- software development.
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Databases.
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Machine learning.
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Maths for computer scientists.
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R (Computer program language)
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SAS (Computer file)
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SAS (Computer file)
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| Dewey Classification
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338.7
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| LC Classification
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HB3730.G878 2018eb
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