Applied analytics through case studies using SAS and R : implementing predictive models and machine learning techniques /: implementing predictive models and machine learning techniques. ([2018])

• Record Type: Book
• Further Information: Note: Deepti Gupta.
• Authors: Gupta, Deepti
• 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
• Publisher Details: Boston, Massachusetts : Apress
• Publication Date: 2018
• Copyright Date: 2018
• Extent: 1 online resource
• Subjects: 338.7; Computer science; Business enterprises -- Evaluation -- Case studies; Machine learning; R (Computer program language); BUSINESS & ECONOMICS / Industries / General; Business enterprises -- Evaluation; Machine learning; R (Computer program language); Computers -- Programming -- Open Source; Computers -- Mathematical & Statistical Software; Business & Economics -- Business Mathematics; Computer programming / software development; Maths for computer scientists; Business mathematics & systems; Big data; Open source software; Computer programming; Business mathematics; Computers -- Database Management -- General; Databases; Electronic books; Case studies
• Languages: English
• ISBNs: 9781484235256; 1484235258
• Related ISBNs: 9781484235249; 148423524X
• Notes: Note: Includes bibliographical references and index.; Note: Vendor-supplied metadata.
• Access Rights: Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
• Access Usage: Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force.