growth in computing power and operational databases

challenges presented by massive, opportunistic data

prediction and understanding of business outcomes

contributing disciplines: statistics, machine learning, pattern recognition

Problem Formulation

formulating business objectives that can be translated into suitable analytical methods

applying predictive modeling to database marketing, credit scoring, fraud detection, and healthcare informatics

applying and recognizing the pitfalls of cluster analysis and association rule discovery

Data Difficulties

data structure and organization

errors, outliers, and missing values

sampling and over sampling

dimension reduction and the curse of dimensionality

Introduction to Enterprise Miner

exploring workspace components

setting up projects

constructing analysis flow diagrams

conducting initial data exploration

employing variable selection techniques

imputing missing values

Regression

performing regression using a target marketing example

examining stepwise regression methods

Neural Networks

constructing multilayer perceptions

visualizing network complexity

performing stopped training

Decision Trees

constructing a decision tree using a credit scoring example

examining the functionality of the Decision Tree node

constructing decision trees with binary and multi way splits

pruning and assessing decision trees

Model Evaluation and Implementation

comparing candidate models

constructing simple ensemble models

generating and using score code

Cluster Analysis

performing cluster analysis using sales data

using the Clustering node for k-means cluster analysis

clustering with self-organizing maps

visualizing clusters using the Insight node

Associations and Sequences

using the Associations node in a consumer banking example

quantifying the associations among items

exploring sequences among items

training.india@sas.com