IBM SPSS Modeler: Techniques for Missing Data Assignment Help

IBM SPSS Modeler is a data mining workbench that allows you to build predictive models quickly and intuitively without programming. SPSS Modeler is typically used by analysts to analyse data by mining historical data and then deploying models to generate predictions for recent (or even real-time) data.
Techniques for Missing Data is a self-paced video series. Students will learn how to identify and handle missing data in Modeler. Students will also learn how to deal with missing data in various ways, such as imputation of missing values, removing missing data, and running parallel streams with and without missing data. Students will also learn how to identify and handle missing data using the Type, Data Audit, and Filler nodes.

KEY FEATURE

IBM SPSS is a software suite that includes several products. It is a platform that allows users to create predictive models and carry out analytics tasks.

It enables business users of all skill levels to perform complex statistical analysis to solve business and research problems quickly and efficiently.

SPSS Statistics is a statistical software platform that provides a robust set of features to assist users in quickly analysing data and understanding large and complex datasets.

With a top-down hypothesis-testing approach, organisations can extract actionable insights. SPSS Modeler is a platform for graphical data science and predictive analytics.

IBM SPSS Modeler: Techniques for Missing Data Assignment Help

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Key Topics

    Students will learn the following things from this course:

    • Understand how missing data is identified and defined in IBM SPSS Modeler
    • Impute missing values
    • Remove missing data
    • Run parallel streams with and without missing data
    • Use the Type, Data Audit, Derive, and Filler nodes to identify and handle missing data

    Handling Missing Values

    In light of your business or domain knowledge, you should decide how to handle missing values. Remove blanks from your data set to reduce training time and improve accuracy. The presence of blank values, on the other hand, may lead to new business opportunities or additional insights.

    a) When deciding on the best technique, keep the following aspects of your data in mind:

    • Size of the data set
    • Number of fields containing blanks
    • Amount of missing information

    b) In general terms, there are two approaches you can follow:

    • You can exclude fields or records with missing values
    • You can impute, replace, or coerce missing values using a variety of methods

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