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Fortino, Andres [Author]

Data mining and predictive analytics for business decisions

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  • Media type: E-Book
  • Title: Data mining and predictive analytics for business decisions : a case study approach
  • Contains: Frontmatter
    Contents
    Preface
    Acknowledgments
    Chapter 1: Data Mining and Business
    Chapter 2: The Data Mining Process
    Chapter 3: Framing Analytical Questions
    Chapter 4: Data Preparation
    Chapter 5: Descriptive Analysis
    Chapter 6: Modeling
    Chapter 7: Predictive Analytics with Regression Models
    Chapter 8: Classification
    Chapter 9: Clustering
    Chapter 10: Time Series Forecasting
    Chapter 11: Feature Selection
    Chapter 12: Anomaly Detection
    Chapter 13: Text Data Mining
    Chapter 14: Working with Large Data Sets
    Chapter 15: Visual Programming
    Index
  • Contributor: Fortino, Andres [VerfasserIn]
  • imprint: Dulles, VA: Mercury Learning and Information, 2023
  • Extent: 1 Online-Ressource (272 p.)
  • Language: English
  • ISBN: 9781683926740
  • Keywords: Data Mining
    Entscheidungsfindung
    Unternehmen > Entscheidung
  • Origination:
  • Footnote: In English
  • Description: 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 machine learning. Most of the exercises use R and Python, but rather than focus on coding algorithms, the book employs interactive interfaces to these tools to perform the analysis. Using the CRISP-DM data mining standard, the early chapters cover conducting the preparatory steps in data mining: translating business information needs into framed analytical questions and data preparation. The Jamovi and the JASP interfaces are used with R and the Orange3 data mining interface with Python. Where appropriate, Voyant and other open-source programs are used for text analytics. The techniques covered in this book range from basic descriptive statistics, such as summarization and tabulation, to more sophisticated predictive techniques, such as linear and logistic regression, clustering, classification, and text analytics. Includes companion files with case study files, solution spreadsheets, data sets and charts, etc. from the book. FEATURES:Covers basic descriptive statistics, such as summarization and tabulation, to more sophisticated predictive techniques, such as linear and logistic regression, clustering, classification, and text analyticsUses R, Python, Jamovi and JASP interfaces, and the Orange3 data mining interfaceIncludes companion files with the case study files from the book, solution spreadsheets, data sets, etc
  • Access State: Restricted Access | Information to licenced electronic resources of the SLUB