Course outline

Introduction to Predictive Modeling Using IBM SPSS Modeler (v18) eLearning

Categories: Guaranteed To Run™, IBM

GK IBM LS Partner

Duration: 6.2 Hours

Contains: instructional and interactive content, demonstrations and hand-on simulated exercises.

This course provides an application-oriented introduction to predictive models. Students will review basic statistical concepts and learn how to examine individual fields, how to assess the relationship between fields, and, finally, how to choose the type of predictive model. The course will present three models, and students will learn about basic options in the dialog boxes and how to interpret the results.

Throughout the course, IBM SPSS Modeler will be used in demonstrations and exercises.

Throughout the course, (fictitious) data from a telecommunications firm will be used as an example. The cases presented can easily be translated to other business domains.

If you are enrolling in a Self Paced Virtual Classroom or Web Based Training course, before you enroll, please review the Self-Paced Virtual Classes and Web-Based Training Classes on our Terms and Conditions page, as well as the system requirements, to ensure that your system meets the minimum requirements for this course.

Please refer to course overview

General computer literacy

  • Anyone who wants to be introduced to predictive modeling.
  • Anyone who intends to take the Predictive Modeling for Categorical Targets Using IBM SPSS Modeler, Predictive Modeling for Continuous Targets Using IBM SPSS Modeler, or Clustering and Association Modeling Using IBM SPSS Modeler course, and has limited or no statistical background.

1:      Introduction to predictive modeling

  • Identify measurement levels
  • Identify modeling objectives
  • Identify three types of models
  • Plan of the course

2:    Examine individual fields

  • Use measures of central tendency and dispersion
  • Explain what are standardized scores (z-scores)
  • Detect outliers in continuous fields
  • Use automated data preparation

3:    Examine the relationship between two fields

  • Identify the steps in statistical testing
  • Examine the relationship between two categorical fields using percentages and the Chi-Square test
  • Examine the relationship between a categorical and continuous field using means and the F test
  • Examine the relationship between two continuous fields using the correlation
  • Identify important predictors with the Feature Selection node

4:    Predictive modeling for a categorical target: CHAID

  • Review the relationship between two categorical field
  • Explain how CHAID builds a decision tree
  • Assess the accuracy of a decision tree
  • Score records with the CHAID model nugget to add propensities

5:    Predictive modeling for a continuous target: Linear regression

  • Review the relationship between two continuous fields.
  • Explain how Linear regression predicts a continuous target
  • Assess the accuracy of a Linear regression model
  • Include categorical predictors in the model

6:    Explore a clustering model to create customer segments

  • Explain how K-Means creates segments
  • Use the K-Means model nugget to score records

7:    Overview of models

  •  Overview of classification models
  •  Overview of segmentation models
  •  Overview of association models




This course is delivered by an authorized IBM Global Training Provider.

Feel free to contact us, if you want to know the price and location of this course. A Digital Revolver representative will contact you shortly to help you with your inquiry.
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  • This schedule icon the schedule indicates that this date/time will be conducted as Instructor Led Training (ILT) or a Virtual Instructor Led Training (VILT) depending on the indicated class availablity.
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