This intermediate course is for IBM SPSS Modeler Analysts who have completed the Introduction to IBM SPSS Modeler and Data Mining course who want to become familiar with the modeling techniques used to classify customers in IBM SPSS Modeler. This includes data analysts and analytics business users.
Classifying Customers Using IBM SPSS Modeler (V16) is an intermediate level course that 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.
Introduction to Classifying Customers List three modeling objectives List two business questions that involve classifying customers Explain the concept of field measurement level and its implications for selecting a modeling technique List three 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 two differences between CHAID, C and R Tree, and Quest Building Your Tree Directly Customize two options in the CHAID node Customize two options in the C and R Tree node Customize two options in the Quest node Customize two options in the C5.0 node Use the Analysis node and the Evaluation node to evaluate and compare models List two differences between CHAID, C and R Tree, Quest, and C5.0 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 List two differences between Discriminant and Logistic Using Machine Learning Models Explain key concepts for Neural Net Customize one option in the Neural Net node
Please refer to Course Overview for description information.