Access this on-demand training session to learn how to build, evaluate and deploy predictive models in IBM SPSS Modeler. Across 90 minutes you will gain a structured, practical understanding of the full modelling lifecycle, from planning using CRISP-DM through to applying models to live data to inform real-time decisions.

The session introduces a range of modelling techniques available in SPSS Modeler and explains how to choose the right approach for your project. You will learn how to define target and input fields correctly, build an interactive decision tree model and use the Auto-Classifier node to compare techniques and identify the most accurate option. The session also covers how to assess model performance using charts and tables, how predictive models can be used to maximise organisational profit and how to deploy a model to generate predictions against current data.

• An overview of the CRISP-DM process – If you’re planning a data mining or modelling project it’s vital to use a robust and well-proven methodology. The CRISP-DM methodology provides a structured approach to planning a data mining project. You’ll gain an insight into each of the elements of the CRISP DM model and understand how you can use it to plan and execute your own data mining and modelling projects.
• An overview of modelling techniques – You’ll learn about the different types of modelling that are possible within SPSS Modeler and how to decide which is most appropriate for your projects.
• How to define model inputs and target fields – Setting up your model by correctly defining which variable you are trying to predict and which variables are suitable to put into your predictive model.
• Creating a predictive model interactively using a decision tree algorithm – How to use one of the most popular and powerful modelling techniques to build a predictive model.
• Using the Auto-Classifier node in SPSS Modeler to compare different modelling techniques – Determining which is the best modelling technique to use for your particular objectives and understanding which model is likely to give you the most accurate results.
• Assessing model performance using charts and tables – Understanding how best to assess the accuracy of your model.
• Using a predictive model to maximise profit – How you can use predictive modelling to enhance the profitability of your organisation.
• Applying a model to generate predictions against current data – Deploying your model to influence the way in which the business makes decisions in real time.