rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Applying Predictive Analytics
    sub title: Finding Value in Data
  name:
    name part: McCarthy, Richard V.
    role:
      role term: creator
    role:
      role term: author.
    role:
      role term: aut
    role:
      role term: http://id.loc.gov/vocabulary/relators/aut
  name:
    name part: McCarthy, Mary M.
    role:
      role term: author.
    role:
      role term: aut
    role:
      role term: http://id.loc.gov/vocabulary/relators/aut
  name:
    name part: Ceccucci, Wendy.
    role:
      role term: author.
    role:
      role term: aut
    role:
      role term: http://id.loc.gov/vocabulary/relators/aut
  name:
    name part: Halawi, Leila.
    role:
      role term: author.
    role:
      role term: aut
    role:
      role term: http://id.loc.gov/vocabulary/relators/aut
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: gw
    date issued: 2019
    edition: 1st ed. 2019.
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: X, 205 p. online resource.
  abstract: This textbook presents a practical approach to predictive analytics for classroom learning. It focuses on using analytics to solve business problems and compares several different modeling techniques, all explained from examples using the SAS Enterprise Miner software. The authors demystify complex algorithms to show how they can be utilized and explained within the context of enhancing business opportunities. Each chapter includes an opening vignette that provides real-life example of how business analytics have been used in various aspects of organizations to solve issue or improve their results. A running case provides an example of a how to build and analyze a complex analytics model and utilize it to predict future outcomes. Focuses on how to use predictive analytic techniques to analyze historical data for the purpose of predicting future results; Takes an applied approach and focus on solving business problems using predictive analytics and features case studies and a variety of examples; Uses examples in SAS Enterprise Miner, one of world's leading analytics software tools.
  table of contents: Introduction to Predictive Analytics -- Know Your Data - Data Preparation -- What do Descriptive Statistics Tell Us -- The First of the Big Three - Regression -- The Second of the Big Three - Decision Trees -- The Third of the Big Three - Neural Networks -- Model Comparisons and Scoring -- Appendix A -- Data Dictionary for the Automobile Insurance Claim Fraud Data Example -- Conclusion.
  note: by Richard V. McCarthy, Mary M. McCarthy, Wendy Ceccucci, Leila Halawi.
  subject:
    topic: Electrical engineering
  subject:
    topic: Computational intelligence
  subject:
    topic: Data mining
  subject:
    topic: Big data
  subject:
    topic: Communications Engineering, Networks
  subject:
    topic: Computational Intelligence
  subject:
    topic: Data Mining and Knowledge Discovery
  subject:
    topic: Big Data/Analytics
  classification: TK1-9971
  classification: 621.382
  related item:
    title info:
      title: Springer eBooks
  identifier: 9783030140380
  identifier: https://doi.org/10.1007/978-3-030-14038-0
  record info:
    record creation date: 190312
    record change date: 20191022151317.0
    record identifier: 978-3-030-14038-0