With SPSS Modeler flows, you can quickly develop predictive models that use business expertise and deploy them into business operations to improve decision making. Designed around the long-established SPSS Modeler client software and the industry-standard CRISP-DM model it uses, the flows interface supports the entire data mining process, from data to better business results.

SPSS Modeler offers various modeling methods that are taken from machine learning, artificial intelligence, and statistics. You can use the methods available on the node palette to derive new information from your data and to develop predictive models. Each method has certain strengths and is best suited for particular types of problems.

Using the Flow Editor, you prepare or shape data, train or deploy a model, or transform data and export it back to a database table or a file in Cloud Object Storage. Running an SPSS Modeler flow consumes capacity unit hours. For more information, see watsonx.ai Studio environments compute usage.

Data format

• Relational: Tables in relational data sources
• Tabular: Tables in data files such as .xls, .csv, .json, or .sas. For Excel files, only the first sheet is read.
• Textual: In the supported relational tables or files

For more information about the file types that are supported, see Supported data sources for SPSS Modeler.

Data size

Any

How can I prepare data?

Use automatic data preparation functions

Write SQL statements to manipulate data

Cleanse, shape, sample, sort, and derive data

How can I analyze data?

Visualize data with many chart options

Identify the natural language of a text field

How can I build models?

Build predictive models

Choose from over 40 modeling algorithms, and many other nodes

Use automatic modeling functions

Model time series or geospatial data

Classify textual data

Identify relationships between the concepts in textual data

Getting started

To create an SPSS Modeler flow from the project's Assets tab, click New asset > Build models as a visual flow.