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0A035G – Advanced Predictive Modeling Using IBM SPSS Modeler

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This course builds on the courses Classifying Customers Using IBM SPSS Modeler (V16) and Predicting Continuous Targets Using IBM SPSS Modeler (V16). 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 as components or factors, is presented. The next two modules focus on advanced predictive models, such as Decision List, Support Vector Machines and Bayes Net. Following this presentation, two modules present methods 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: $700 CAD

Available Course Formats:

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

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Course: 0A035G – Advanced Predictive Modeling Using IBM SPSS Modeler

Preparing Data for Modeling

  • Addressing general data quality issues
  • Handling anomalies
  • Selecting important predictors
  • Partitioning the data to better evaluate models
  • Balancing the data to build better models

Reducing Data with PCA/Factor

  • Explain the basic ideas behind PCA/Factor
  • Customize two options in the PCA/Factor node

Using Decision List to Create Rulesets

  • Explain how Decision List builds a ruleset
  • Using Decision List interactively
  • Creating rulesets directly with Decision List

Advanced Predictive Models

  • Explain the basic ideas behind SVM
  • Customizing two options in the SVM node
  • Explain the basic ideas behind Bayes Net
  • Customizing two options in the SVM node

Combining Models

  • Using the Ensemble node to combine model predictions
  • Improving the model performance by meta-level modeling

Finding the Best Predictive Model

  • Find the best model for categorical targets

Audience

This intermediate-level course is for users of IBM SPSS Modeler responsible for building predictive models (also known as classification models).

Prerequisites

You should have:

  • Completion of the course Introduction to IBM SPSS Modeler and Data Mining OR experience in analyzing data with IBM SPSS Modeler
  • Familiarity with basic modeling techniques, either through completions of the courses Classifying Customers Using IBM SPSS Modeler AND Predicting Continuous Targest 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.

On-Demand

This course is available on-demand: request a specific course, location, time frame and/or format by completing this form. This offering increases schedule flexibility and allows us to accommodate people with different availabilities.

Customized Courses

On-site courses can also be customized based on user roles and needs within your organization. This option allows lower costs in travel and time and increased flexibility in scheduling. This format also enables students to work with the organization's data - adding value to the learning experience. This makes the training relevant and applicable to the students; daily tasks and therefore, results in a quicker path to productivity.