Data Mining and Predictive Analytics for Business Decisions : A Case Study Approach

With many recent advances in data science, we have many more tools and techniques available for data analysts to extract information from data sets. This book will assist data analysts to move up from simple tools such as Excel for descriptive analytics to answer more sophisticated questions using m...

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Bibliographic Details
Main Author Fortino, Andres
Format Electronic eBook
LanguageEnglish
Published Dulles, VA : Mercury Learning & Information, [2023]
Subjects
Data mining.
Electronic data processing.
elektronické knihy
electronic books
Online AccessFull text
ISBN9781683926740
1683926749
1683926730
9781683926733
Physical Description1 online resource (291 p.)

Cover

Table of Contents:
  • Cover
  • Title Page
  • Copyright
  • Dedication
  • Contents
  • Preface
  • Acknowledgments
  • Chapter 1: Data Mining and Business
  • Data Mining Algorithms and Activities
  • Data is the New Oil
  • Data-Driven Decision-Making
  • Business Analytics and Business Intelligence
  • Algorithmic Technologies Associated with Data Mining
  • Data Mining and Data Warehousing
  • Case Study 1.1: Business Applications of Data Mining
  • Case A
  • Classification
  • Case B
  • Regression
  • Case C
  • Anomaly Detection
  • Case D
  • Time Series
  • Case E
  • Clustering
  • Reference
  • Chapter 2: The Data Mining Process
  • Data Mining as a Process
  • Exploration
  • Analysis
  • Interpretation
  • Exploitation
  • Selecting a Data Mining Process
  • The CRISP-DM Process Model
  • Business Understanding
  • Data Understanding
  • Data Preparation
  • Modeling
  • Evaluation
  • Deployment
  • Selecting Data Analytics Languages
  • The Choices for Languages
  • References
  • Chapter 3: Framing Analytical Questions
  • How Does CRISP-DM Define the Business and Data Understanding Step?
  • The World of the Business Data Analyst
  • How Does Data Analysis Relate to Business Decision-Making?
  • How Do We Frame Analytical Questions?
  • What Are the Characteristics of Well-framed Analytical Questions?
  • Exercise 3.1
  • Framed Questions About the Titanic Disaster
  • Case Study 3.1
  • The San Francisco Airport Survey
  • Case Study 3.2
  • Small Business Administration Loans
  • References
  • Chapter 4: Data Preparation
  • How Does CRISP-DM Define Data Preparation?
  • Steps in Preparing the Data Set for Analysis
  • Data Sources and Formats
  • What is Data Shaping?
  • The Flat-File Format
  • Application of Tools for Data Acquisition and Preparation
  • Exercise 4.1
  • Shaping the Data File
  • Exercise 4.2
  • Cleaning the Data File
  • Ensuring the Right Variables are Included
  • Using SQL to Extract the Right Data Set from Data Warehouses
  • Case Study 4.1: Cleaning and Shaping the SFO Survey Data Set
  • Case Study 4.2: Shaping the SBA Loans Data Set
  • Case Study 4.3: Additional SQL Queries
  • Reference
  • Chapter 5: Descriptive Analysis
  • Getting a Sense of the Data Set
  • Describe the Data Set
  • Explore the Data Set
  • Verify the Quality of the Data Set
  • Analysis Techniques to Describe the Variables
  • Exercise 5.1
  • Descriptive Statistics
  • Distributions of Numeric Variables
  • Correlation
  • Exercise 5.2
  • Descriptive Analysis of the Titanic Disaster Data
  • Case Study 5.1: Describing the SFO Survey Data Set
  • Solution Using R
  • Solution Using Python
  • Case Study 5.2: Describing the SBA Loans Data Set
  • Solution Using R
  • Solution Using Python
  • Reference
  • Chapter 6: Modeling
  • What is a Model?
  • How Does CRISP-DM Define Modeling?
  • Selecting the Modeling Technique
  • Modeling Assumptions
  • Generate Test Design
  • Design of Model Testing
  • Build the Model
  • Parameter Setting
  • Models
  • Model Assessment
  • Where Do Models Reside in a Computer?
  • The Data Mining Engine

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