Applying predictive analytics : finding value in data / Richard V. McCarthy, Mary M. McCarthy, Wendy Ceccucci, Leila Halawi.
McCarthy, Richard V. author.; McCarthy, Mary M. author.; Ceccucci, Wendy, author.; Halawi, Leila, author.
2019
QA76.9.D343
Available Online

Linked e-resources

Linked Resource Online Access
Concurrent users Unlimited
Authorized users Authorized users
Document Delivery Supplied Can lend chapters, not whole ebooks

Details

Title Applying predictive analytics : finding value in data / Richard V. McCarthy, Mary M. McCarthy, Wendy Ceccucci, Leila Halawi.
Author McCarthy, Richard V. author.
ISBN 9783030140380 (electronic book)
3030140385 (electronic book)
9783030140373
3030140377
DOI https://doi.org/10.1007/978-3-030-14038-0
Published Cham, Switzerland : Springer, 2019.
Language English
Description 1 online resource (x, 205 pages)
Item Number 10.1007/978-3-030-14038-0 doi
Call Number QA76.9.D343
Dewey Decimal Classification 006.3/12
Summary 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.
Bibliography, etc. Note Includes bibliographical references and index.
Access Note Access limited to authorized users.
Source of Description Online resource; title from PDF title page (SpringerLink, viewed March 27, 2019).
Added Author McCarthy, Mary M. author.
Ceccucci, Wendy, author.
Halawi, Leila, author.
Available in Other Form Print version: 9783030140373
Linked Resources Online Access
Record Appears in Online Resources > Ebooks
All Resources
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.

Browse Subjects

Show more subjects...

Statistics

from
to
Export