IBM SPSS Modeler: Techniques for Missing Data

IBM SPSS Modeler: Techniques for Missing Data

IBM SPSS Modeler is a data mining workbench that allows you to build predictive models quickly and intuitively without programming. Analysts typically use SPSS Modeler to analyze data by mining historical data and then deploying models to generate predictions for recent (or even real-time) data.

Overview: Techniques for Missing Data is a series of self-paced videos (three hours of content). Students will learn how missing data is identified and handled in Modeler. Students also will learn different approaches to dealing with missing data including imputation of missing values, removing missing data, and running parallel streams with and without missing data. Students will also learn how to use the Type, Data Audit, and Filler nodes to identify and handle missing data.

IBM SPSS Modeler Seminar Series

Url: View Details

What you will learn

• Understand how missing data is identified and defined in IBM SPSS Modeler
• Impute missing values
• Remove missing data

Rating: 4

Level: Intermediate Level

Duration: 3.5 hours

Instructor: Sandy Midili