IBM SPSS Modeler: Techniques for Missing Data

Unlock the power of predictive analytics with IBM SPSS Modeler, a leading data mining workbench. This intuitive platform empowers analysts to build predictive models rapidly – no programming required! Leverage historical data to gain valuable insights and deploy powerful models to generate accurate predictions for current and real-time data.

Techniques for Missing Data is a comprehensive, self-paced video series (3 hours total) designed to equip you with the skills to effectively manage incomplete datasets within SPSS Modeler. Learn how to identify missing data and apply various strategies for handling it.

Here's what you'll discover:

• Missing Data Identification: Master the techniques for pinpointing missing values in your data within SPSS Modeler.
• Handling Missing Data Approaches: Explore diverse methods for dealing with missing data, including:
  • Imputation of Missing Values: Learn how to fill in missing data points with estimated values.
  • Removing Missing Data: Understand when and how to remove records with missing data.
  • Parallel Streams: Develop strategies for running analyses with and without missing data to compare results.
• SPSS Modeler Nodes for Missing Data: Become proficient in using key nodes:
  • Type Node: Utilize the Type node to understand data types and identify potential issues.
  • Data Audit Node: Employ the Data Audit node to gain a deeper understanding of your data quality, including missing values.
  • Filler Node: Effectively use the Filler node to impute missing values using various statistical methods.

This training will enable you to create more robust and reliable predictive models by effectively addressing the challenges posed by missing data in your data analysis projects.