Chapter 1: Introduciton to Data Mining and Predictive Analytics
Introduction to data mining
CRISP-DM overview
Bussiness Understanding
Data Understanding
Data Preparation
Modeling
Evaluation
Deployment
Learning more about CRISP-DM
The data ming process(as a case study)
Summary
Chatper 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 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 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 adress misiing 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-Foumula
Derive-Flag
Derive-Nominal
Dervie-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 ndoes
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 asessment using the Analysis node
Modifying CHAID settings
Model comparision using the Analysis node
Model assessment and comparsion using the Evaluation node
Scoring new data
Exporting Predictions
Summary
INDEX
Introduction to data mining
CRISP-DM overview
Bussiness Understanding
Data Understanding
Data Preparation
Modeling
Evaluation
Deployment
Learning more about CRISP-DM
The data ming process(as a case study)
Summary
Chatper 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 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 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 adress misiing 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-Foumula
Derive-Flag
Derive-Nominal
Dervie-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 ndoes
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 asessment using the Analysis node
Modifying CHAID settings
Model comparision using the Analysis node
Model assessment and comparsion using the Evaluation node
Scoring new data
Exporting Predictions
Summary
INDEX