Applied Analytics Using SAS Enterprise Miner 5.3

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Applied Analytics Using SAS Enterprise Miner 5.3

Overview

Prerequisites

Course Outline

Course Materials

System Requirements

Course Outline

Introduction

touring SAS Enterprise Miner 5.3
placing SAS Enterprise Miner in the analysis workflow
application examples and case studies
pattern discovery and predictive modeling

Accessing and Assaying Prepared Data

defining a SAS Enterprise Miner 5.3 project
defining a data source
validating source data

Introduction to Predictive Modeling with Decision Trees

defining a modeling data source
partitioning data for model development
growing a decision tree with the Desktop Tree Application
running the Decision Tree node
using Decision Tree node options
understanding predictive modeling results data

Introduction to Predictive Modeling with Regressions

comparing linear and logistic regression
using the Regression node
imputing missing values with the Impute node
replacing data values with the Replacement node
performing input selection
understanding regression modeling output
extending regression models with polynomial and interaction terms

Introduction to Predictive Modeling with Neural Networks and Other Modeling Tools

introduction to neural network (multilayer perceptron) models
using the Neural Network node
performing model selection with the AutoNeural node (self-study)
other SAS Enterprise Miner 5.3 modeling tools (self-study)

Model Assessment

defining a prior vector
effects of prior vectors on model development
changing model selection criteria
defining a profit matrix
comparing models with the Model Comparison node
introduction to model assessment statistics

Model Implementation

defining a Score data set
scoring a data set with the Score node
using a SAS Code node to export scored data
generating and using score code

Introduction to Pattern Discovery

clustering and segmenting data
using the Transform Variables node
using the Clustering node for k-means cluster analysis
applying association and sequence discovery (self-study)
using the Associations node in a consumer banking example (self-study)
quantifying the associations among items (self-study)
exploring sequences among items (self-study)

Special Topics (Self-Study)

reducing irrelevancy
reducing redundancy
combining models with the Ensemble node
consolidating categorical inputs with the Decision Tree node
explaining complex models with the Decision Tree node

Case Studies (Self-Study)

segmenting bank customer transaction histories
association analysis on Web services data
creating a simple risk model from consumer loan data
predicting university enrollment
creating a response model for an insurance product