Duration

3 days

Overview

This is the self paced training version of "Predictive Modeling with IBM SPSS Modeler" classroom course. This self paced training course demonstrates how to develop models to predict categorical and continuous outcomes, using such techniques as neural networks, decision trees, logistic regression, support vector machines, and Bayesian network models. Use of the binary classifier and numeric predictor nodes to automate model selection is included. Feature selection and detection of outliers are discussed. Expert options for each modeling node are reviewed in detail and advice is provided on when and how to use each model. You will also learn how to combine two or more models to improve prediction.

Audience

This course follows either "Introduction to IBM SPSS Modeler and Data Mining" or Advanced Data Preparation with IBM SPSS Modeler is essential for anyone who wishes to become familiar with the full range of modeling techniques available in IBM SPSS Modeler to create predictive models.

Prerequisites

You should have:

General computer literacy.
Experience using IBM SPSS Modeler (formerly Clementine), including
familiarity with the IBM SPSS Modeler environment,
creating streams,
reading in data files,
assessing data quality and handling missing data (including the type and data audit nodes),
basic data manipulation (including the derive and select nodes),
and creation of models.

Prior completion of Introduction to IBM SPSS Modeler and Data Mining is required and completion of Advanced Data Preparation with IBM SPSS Modeler is strongly encouraged.

An introductory course in statistics, or equivalent experience, would be helpful for the statistics-based modeling techniques.

Objectives

Please refer to course overview.

Key topics

Preparing data for modeling

Searching for data anomalies

Selecting predictors

Data reduction with principal components

Neural networks

Support vector machines

Cox regression

Time series analysis

Decision trees

Linear regression

Logistic regression

Discriminant analysis

Bayesian networks

Numeric Predictor node

Binary Classifier Node

Combining models to improve performance

Getting the most from models

Appendix A: Decision List

Programme scope, delivery arrangements and suitability are confirmed during enquiry.