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0E032G – Predictive Modeling with IBM SPSS Modeler SPVC

Home / 0E032G – Predictive Modeling with IBM SPSS Modeler SPVC

Predictive Modeling with IBM SPSS Modeler demonstrates how to develop models to predict categorical and continuous outcomes, using such techniques as neural networks, decision trees, logistic regression, support vector machines, and Bayesian network models. Use of the binary classifier and numeric predictor nodes to automate model selection is included. Feature selection and detection of outliers are discussed. Expert options for each modeling node are reviewed in detail and advice is provided on when and how to use each model. Students will also learn how to combine two or more models to improve prediction.

 

Please note: This course is only available in the self-paced format. The in-class version of this training has been retired by IBM, and has been replaced by 0A0U5 - Classifying Customers Using IBM SPSS Modeler.

 

Course Length: 3 day(s)

Course Price: $1155 CAD

Available Course Formats:

  • Self-Paced Virtual Classroom

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Course: 0E032G – Predictive Modeling with IBM SPSS Modeler SPVC

Preparing data for modeling

Searching for data anomalies

Selecting predictors

Data reduction with principal components

Neural networks

Support vector machines

Cox regression

Time series analysis

Decision trees

Linear regression

Logistic regression

Discriminant analysis

Bayesian networks

Numeric Predictor node

Binary Classifier Node

Combining models to improve performance

Getting the most from models

Appendix A: Decision List

Audience

This advanced course follows either "Introduction to IBM SPSS Modeler and Data Mining" or Advanced Data Preparation with IBM SPSS Modeler is essential for anyone who wishes to become familiar with the full range of modeling techniques available in IBM SPSS Modeler to create predictive models.

Prerequisites

  • General computer literacy
  • Experience using IBM SPSS Modeler (formerly Clementine) , including familiarity with the IBM SPSS Modeler environment, creating streams, reading in data files, assessing data quality and handling missing data (including the type and data audit nodes), basic data manipulation (including the derive and select nodes), and creation of models.

You should complete:

  • Introduction to IBM SPSS Modeler and Data Mining

Completion of Advanced Data Preparation with IBM SPSS Modeler is strongly encouraged. An introductory course in statistics, or equivalent experience, would be helpful for the statistics-based modeling techniques.

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.