Predictive Modeling for Continuous Targets Using IBM SPSS Modeler (v18.1.1)
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.
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 available in IBM SPSS Modeler to predict a continuous target.
• Experience using IBM SPSS Modeler including familiarity with the Modeler environment, creating streams, reading data files, exploring data, setting the unit of analysis, combining datasets, deriving and reclassifying fields, and a basic knowledge of modeling.
• Prior completion of Introduction to IBM SPSS Modeler and Data Science (v18.1.1) is recommended.
Please refer to course overview
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