IBM SPSS Modeler essentials effective techniques for builing powerful data minng and predictive analytics solutions
Get to grips with the fundamentals of data mining and predictive analytics with IBM SPSS Modeler About This Book Get up?and-running with IBM SPSS Modeler without going into too much depth. Identify interesting relationships within your data and build effective data mining and predictive analytics so...
| Otros Autores: | , |
|---|---|
| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Birmingham, England :
Packt
2017.
|
| Edición: | 1st edition |
| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009630251106719 |
Tabla de Contenidos:
- Cover
- Copyright
- Credits
- About the Authors
- About the Reviewer
- www.PacktPub.com
- Customer Feedback
- Dedication
- Table of Contents
- Preface
- Chapter 1: Introduction to Data Mining and Predictive Analytics
- Introduction to data mining
- CRISP-DM overview
- Business Understanding
- Data Understanding
- Data Preparation
- Modeling
- Evaluation
- Deployment
- Learning more about CRISP-DM
- The data mining process (as a case study)
- Summary
- Chapter 2: The Basics of Using IBM SPSS Modeler
- Introducing the Modeler graphic user interface
- Stream canvas
- Palettes
- Modeler menus
- Toolbar
- Manager tabs
- Project window
- Building streams
- Mouse buttons
- Adding nodes
- Editing nodes
- Deleting nodes
- Building a stream
- Connecting nodes
- Deleting connections
- Modeler stream rules
- Help options
- Help menu
- Dialog help
- Summary
- Chapter 3: Importing Data into Modeler
- Data structure
- Var. File source node
- Var. File source node File tab
- Var. File source node Data tab
- Var. File source node Filter tab
- Var. File source node Types tab
- Var. File source node Annotations tab
- Viewing data
- Excel source node
- Database source node
- Levels of measurement and roles
- Summary
- Chapter 4: Data Quality and Exploration
- Data Audit node options
- Data Audit node results
- The Quality tab
- Missing data
- Ways to address missing data
- Defining missing values in the Type node
- Imputing missing values with the Data Audit node
- Summary
- Chapter 5: Cleaning and Selecting Data
- Selecting cases
- Expression Builder
- Sorting cases
- Identifying and removing duplicate cases
- Reclassifying categorical values
- Summary
- Chapter 6: Combining Data Files
- Combining data files with the Append node
- Removing fields with the Filter node.
- Combining data files with the Merge node
- The Filter tab
- The Optimization tab
- Summary
- Chapter 7: Deriving New Fields
- Derive - Formula
- Derive - Flag
- Derive - Nominal
- Derive - Conditional
- Summary
- Chapter 8: Looking for Relationships Between Fields
- Relationships between categorical fields
- Distribution node
- Matrix node
- Relationships between categorical and continuous fields
- Histogram node
- Means node
- Relationships between continuous fields
- Plot node
- Statistics node
- Summary
- Chapter 9: Introduction to Modeling Options in IBM SPSS Modeler
- Classification
- Categorical targets
- Numeric targets
- The Auto nodes
- Data reduction modeling nodes
- Association
- Segmentation
- Choosing between models
- Summary
- Chapter 10: Decision Tree Models
- Decision tree theory
- CHAID theory
- How CHAID processes different types of input variables
- Stopping rules
- Building a CHAID Model
- Partition node
- Overfitting
- CHAID dialog options
- CHAID results
- Summary
- Chapter 11: Model Assessment and Scoring
- Contrasting model assessment with the Evaluation phase
- Model assessment using the Analysis node
- Modifying CHAID settings
- Model comparison using the Analysis node
- Model assessment and comparison using the Evaluation node
- Scoring new data
- Exporting predictions
- Summary
- Index.
