Predictive Modeling with IBM SPSS Modeler

Course Length: 3 Days  |  Price: $1,795  |  Please contact us today to schedule your training

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This course 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. You will also learn how to combine two or more models to improve prediction.

Intended 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.
  • Prior completion of Introduction to IBM SPSS Modeler and Data Mining is required and 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.

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  • 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
  • Use Model Query Subjects to Merge Snowflake Query Subjects
  • Create True Facts and True Dimensions using Model Query Subjects
  • Understand Conformed Dimensions and Query Generation
  • 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
  • Module 1: Preparing Data for Modeling
  • Module 2: Data Reduction: Principal Components
  • Module 3: Decision Trees/Rule Induction
  • Module 4: Neural Networks
  • Module 5: Support Vector Machines
  • Module 6: Linear Regression
  • Module 7: Cox Regression for Survival Data
  • Module 8: Time Series Analysis
  • Module 9: Logistic Regression
  • Module 10: Discriminant Analysis
  • Module 11: Bayesian Networks
  • Module 12: Finding the Best Model for Categorical Targets
  • Module 13: Finding the Best Model for Continuous Targets
  • Module 14: Getting the Most from Models
  • Appendix A: Decision List