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Fortino, Andres
[Verfasser:in]
Data mining and predictive analytics for business decisions
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- Medientyp: E-Book
- Titel: Data mining and predictive analytics for business decisions : a case study approach
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Enthält:
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
- Beteiligte: Fortino, Andres [Verfasser:in]
-
Erschienen:
Dulles, VA: Mercury Learning and Information, 2023
- Umfang: 1 Online-Ressource (272 p.)
- Sprache: Englisch
- ISBN: 9781683926740
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Schlagwörter:
Data Mining
Entscheidungsfindung
Unternehmen > Entscheidung
- Entstehung:
-
Anmerkungen:
In English
- Beschreibung: 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
- Zugangsstatus: Eingeschränkter Zugang | Informationen zu lizenzierten elektronischen Ressourcen der SLUB