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0A037G – Advanced Predictive Modeling Using IBM SPSS Modeler

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This course builds on the courses Predictive Modeling for Categorical Targets Using IBM SPSS Modeler (v18) and Predictive Modeling for Continuous Targets Using IBM SPSS Modeler (v18). It presents advanced techniques to predict categorical and continuous targets. Before reviewing the modeling techniques, data preparation issues are addressed such as partitioning and detecting anomalies. Also, a method to reduce the number of fields to a number of core fields, referred to as components or factors, is presented. Advanced classification models, such as Decision List, Support Vector Machines and Bayes Net, are reviewed. Methods are presented to combine individual models into a single model in order to improve predictive power, including running and evaluating many models in a single run, both for categorical and continuous targets.

Course Length: 1 day(s)

Course Price: $750 CAD

Available Course Formats:

  • In-class
  • Instructor Led Online
  • Self-Paced Virtual Classroom

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Course: 0A037G – Advanced Predictive Modeling Using IBM SPSS Modeler

Course Outline

1. Preparing Data for Modeling
-   Address general data quality issues
-   Handle anomalies
-   Select important predictors
-   Partition the data to better evaluate models
-   Balance the data to build better models

2. Reducing Data with PCA/Factor
-   Explain the basic ideas behind PCA/Factor
-   Customize two options in the PCA/Factor node

3. Using Decision List to Create Rulesets
-   Explain how Decision List builds a ruleset
-   Use Decision List interactively
-   Create rulesets directly with Decision List

4. Exploring advanced predictive models
-   Explain the basic ideas behind SVM
-   Customize two options in the SVM node
-   Explain the basic ideas behind Bayes Net
-   Customize two options in the SVM node

5. Combining Models
-   Use the Ensemble node to combine model predictions
-   Improve the model performance by meta-level modeling

6. Finding the Best Predictive Model
-   Find the best model for categorical targets with AutoClassifier
-   Find the best model for continuous targets with AutoNumeric

Audience

Users of IBM SPSS Modeler responsible for building predictive models who want to leverage the full potential of classification models in IBM SPSS Modeler.

Prerequisites

- General computer literacy

- Experience using IBM SPSS Modeler including familiarity with the Modeler environment, creating streams, reading data files, exploring data, setting the unit of analysis, combining datasets, deriving and reclassifying fields, and basic knowledge of modeling.

- Prior completion of Introduction to Predictive Models using IBM SPSS Modeler (v18) is recommended.

- Familiarity with basic modeling techniques, either through completion of the courses Predictive Modeling for Categorical Targets Using IBM SPSS Modeler and/or Predictive Modeling for Continuous Targets Using IBM SPSS Modeler, or by experience with predictive models in IBM SPSS Modeler.

Instructor Led In Classroom

Newcomp can directly deliver  IBM Business Analytics courses for Business Intelligence, Performance Management, and IBM Advanced Analytics through the use of in-class training facilities.

Currently,  in-class courses are offered in Markham, Ottawa, Vancouver, Halifax, and Edmonton. Please note that classes can be added to new areas based on demand.

Instructor Led Online

Students receive the same quality as an in-class course, with a live instructor and the ability to participate in hands-on labs through real-life examples

ILOs help cut costs by reducing time and travel as they can be taken from home or the office and require only the use of a computer, high-speed wired internet and a headset.

Self Paced

Students can receive the same high-quality training, with the same courseware at their own speed and schedule with SPVC.  Individuals with busy schedules can complete a course over a 30-day timeframe at a lower price than in-class or ILO courses. Please note that there is no live interaction with an instructor in this format.