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Classifying Customers Using IBM SPSS Modeler

Overview

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This one day course provides an overview of how to use IBM SPSS Modeler to predict the category to which a customer belongs. Students will be exposed to rule induction models such as CHAID and C and R Tree. They will also be introduced to traditional statistical models and machine learning models. Business use case examples include: predict whether a customer switches to another provider/brand and whether a customer responds to a particular advertising campaign. Although this course focuses on classifying customers (including students, patients, employees, and so forth), the techniques can also be applied to business questions such as predicting breakdown of machine parts.

Prerequisites

Additional Courses

Follow-On Courses

Key Topics

Introduction to Classifying Customers

  • List modeling objectives
  • List business questions that involve classifying customers
  • Explain the concept of field measurement level and its implications for selecting a modeling technique
  • List types of models to classify customers
  • Determine the classification model to use

Building Your Tree Interactively with CHAID

  • Explain how CHAID grows a tree
  • Build a customized model using CHAID
  • Evaluate a CHAID model by means of accuracy, risk, response and gain
  • Use the model nugget to score records

Building Your Tree Interactively with C&R Tree and Quest

  • Explain how C and R Tree grows a tree
  • Explain how Quest grows a tree
  • Build a model interactively using C and R Tree and Quest
  • List differences between CHAID, C and R Tree, and Quest

Building Your Tree Directly

  • Customize options in the CHAID node
  • Customize options in the C and R Tree node
  • Customize options in the Quest node
  • Customize options in the C5.0 node
  • Use the Analysis node and the Evaluation node to evaluate and compare models
  • List differences between CHAID, C and R Tree, Quest, and C5.0

Using Traditional Statistical Models

  • Explain key concepts for Discriminant
  • Customize options in the Discriminant node
  • Explain key concepts for Logistic
  • Customize options in the Logistic node
  • List differences between Discriminant and Logistic

Using Machine Learning Models

  • Explain key concepts for Neural Net
  • Customize one option in the Neural Net node

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