1: Introduction to predictive models for continuous targets • List three modeling objectives • List two business questions that involve predicting continuous targets • Explain the concept of field measurement level and its implications for selecting a modeling technique • List three types of models to predict continuous targets • Determine the classification model to use 2: Building decision trees interactively • Explain how CHAID grows a tree • Explain how C&R Tree grows a tree • Build CHAID and C&R Tree models interactively • Evaluate models for continuous targets • Use the model nugget to score records 3: Building decision trees directly • Customize two options in the CHAID node • Customize two options in the C&R Tree node • List one difference between CHAID and C&R Tree 4. Using traditional statistical models • Explain key concepts for Linear • Customize options in the Linear node • Explain key concepts for Cox • Customize options in the Cox node 5: Using machine learning models • Explain key concepts for Neural Net • Customize one option in the Neural Net node

This course provides an overview of how to use IBM SPSS Modeler to predict a target field that describes numeric values. Students will be exposed to rule induction models such as CHAID and C&R Tree. They will also be introduced to traditional statistical models such as Linear Regression. Students are introduced to machine learning models, such as Neural Networks. Business use case examples include: predicting the length of subscription for newspapers, telecommunication, and job length, as well as predicting insurance claim amounts.