Course Code : 0A0U7G

This course (formerly Classifying Customers Using IBM SPSS Modeler) focuses on analytical models to predict a categorical field (churn, fraud, response to a mailing, pass/fail exams, machine break-down, and so forth). Students will be introduced to decision trees such as CHAID and C&R Tree, traditional statistical models such as Logistic Regression, and machine learning models such as Neural Networks. The student will learn about important options in dialog boxes, how to interpret the results, and explain the major differences between the models.

• Analytics business users who have completed the Introduction to IBM SPSS Modeler and Data Mining course and who want to become familiar with analytical models to predict a categorical field (yes/no churn, yes/no fraud, yes/no response to a mailing, pass/fail exams, yes/no machine break-down, and so forth).

• Experience using IBM SPSS Modeler, including familiarity with the IBM SPSS Modeler environment, creating streams, importing data (Var. File node), basic data preparation (Type node, Derive node, Select node), reporting (Table node, Data Audit node), and creation of models.

• Prior completion of Introduction to Predictive Modeling Using IBM SPSS Modeler (v18) (0D007) is recommended.

Please refer to course overview.

1. Introduction to predictive modeling for categorical targets

• Identify three modeling objectives

• List three types of models to predict categorical targets

• Explain the concept of field measurement level and its implications for selecting a modeling technique

2. Building decision trees interactively with CHAID

• Explain how CHAID grows decision trees

• Build a customized model with CHAID

• Use the model nugget to score records

• Evaluate a model by means of accuracy, risk, response and gain

3. Building decision trees interactively with C&R Tree and Quest

• Explain how C&R Tree grows a tree

• Build a customized model using C&R Tree and Quest

• Explain how Quest grows a tree

• List two differences between CHAID, C&R Tree, and Quest

4. Building decision trees directly

• Customize two options in the CHAID node

• Customize two options in the C&R Tree node

• Use the Analysis node and Evaluation node to evaluate and compare models

• Customize two options in the Quest node

• Customize two options in the C5.0 node

• List two differences between CHAID, C&R Tree, Quest, and C5.0

5. Using traditional statistical models

• Explain key concepts for Discriminant

• Customize one option in the Discriminant node

• Explain key concepts for Logistic

• Customize one option in the Logistic node

6. Using machine learning models

• Explain key concepts for Neural Net

• Customize one option in the Neural Net node

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