
Define data mining, advanced analytics, and prescriptive analytics using IBM SPSS Modeler to build predictive models and deploy them on historical data.
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.
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.
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.
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.
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.
IBM SPSS Modeler is a data mining workbench that helps you build predictive models quickly and intuitively, without programming. Analysts typically use SPSS Modeler to analyze data by doing data mining and then deploying models.
Overview: This course introduces students to data mining and to the functionality available within IBM SPSS Modeler. The series of stand-alone videos, are designed to introduce students to specific nodes or data mining topics. Each video consists of detailed instructions explaining why we are using a technique, in what situations it is used, how to set it up, and how to interpret the results. This course is broken up into phases. The Introduction to Data Mining Phase is designed to get you up to speed on the idea of data mining. You will also learn about the CRISP-DM methodology which will serve as a guide throughout the course and you will also learn how to navigate within Modeler. The Data Understanding Phase addresses the need to understand what your data resources are and the characteristics of those resources. We will discuss how to read data into Modeler. We will also focus on describing, exploring, and assessing data quality. The Data Preparation Phase discusses how to integrate and construct data. While the Modeling Phase will focus on building a predictive model. The Evaluation Phase focuses how to take your data mining results so that you can achieve your business objectives. And finally the Deployment Phase allows you to do something with your findings.