The workflow in SPSS Modeler is built around the
Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology. This methodology
embeds your work in SPSS Modeler in a larger project with
several phases. The phases were you work in SPSS Modeler use
projects to manage your work and assets.
Figure 1. Phases for SPSS Modeler projects
Phases for data mining
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The CRISP-DM methodology has the following phases.
Business understanding
During this phase, try to gain as much insight as possible into the business
goals for data mining. Meet with stakeholders and determine how your work
with SPSS Modeler addresses business objectives
or problems.
You need to collect and understand your data before you build flows in
SPSS Modeler. Take the time to understand
the data structure, relationships, and patterns in your data.
You need to prepare your data before you train models in SPSS Modeler. Take the time to process your data so
that it is optimized for use in data mining.
Evaluate the quality of your models and their predictions. For example, you
can add Analysis nodes to your flows to assess how
accurate your model's predictions are. You can also use an
Evaluation node to compare predictive models and
find the best one.
Working with projects and data assets
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All your work with SPSS Modeler is done within a project.
A project holds all your data assets and flows.
You can import a stream (.str) that was created in SPSS Modeler
Subscription or SPSS Modeler
client. If the imported flow contains one or more import or export nodes, you are
prompted to convert the nodes when you open the flow.
You can use scripting in SPSS Modeler to automate tasks.
You can write scripts in R, Python, or Python for Spark, and Control Language for
Expression Manipulation (CLEM). CLEM is a language for analyzing and manipulating
the data streams through your flows.
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