IBM SPSS Modeler: Getting Started

Learn how to use the variable file node to import delimited text data, set field names and delimiters, preview results, and adjust encoding and quotes.

Use the Excel source node to import data from Excel, selecting Excel 2007–2010 and a worksheet by index or name, then specify a range or named range and verify.

Explore how the type node in IBM SPSS Modeler defines level of measurement, missing values, and field roles to prepare data for modeling.

Use the data audit node to inspect distributions, missing values, and outliers with field graphs and statistics. Customize fields, view level of measurement, and examine unique and valid values.

Explore the data audit mode and quality tab to assess missing data and outliers, and learn to impute missing values and coerce or discard extreme scores.

Apply the derive node as a formula to create new fields from existing data, using the expression builder in single mode to compute items sold from stereos, tvs, and speakers.

Create a premium flag by using a derive node to generate a new boolean field based on items sold, marking customers as true if 100 or more items are purchased.

Derive a nominal field in IBM SPSS Modeler using the derive node to classify records into low, medium, and high sales categories based on items sold via the expression builder.

Derive a new field with a conditional derive node to classify customers as valued or regular using if-then statements based on years as a customer or premium status.

Explore the append node to combine datasets with similar structure from different time periods, matching fields by name, and producing a unified dataset with a source year tag.

Merge node in IBM SPSS Modeler combines multiple data files into a dataset using key-based joins, enabling inner, outer, or anti joins to unify account, demographic, and feature data.

Use the partition node to split data into training and testing sets, or training, testing, and validation, to train a model and assess generalization.

CHAID uses chi square automatic interaction detection to build classification trees that predict field status from predictors like premier and payment method, distinguishing current customers from churn.

Learn to build a CHAID single-tree model in IBM SPSS Modeler using a partitioned dataset, selecting predictors, defining the target, and evaluating predictions with predictor importance and confidence.

Utilize the analysis node to evaluate predictions for categorical outcomes, compare models by accuracy and consistency, and view confusion matrices and training versus testing performance.

Explain how the evaluation node compares predictive models using gains and lift charts, highlighting baseline and perfect prediction models to assess churn outcomes.

Explore using the flat file export node in IBM SPSS Modeler to export data to a delimited text file, including field names and encoding options.