Predictive Modelling using Logistic Regression

Duration

2 days

Description

This course is designed for predictive modellers and data analysts with basic SAS programming experience. The issues and techniques discussed in this course are directed towards database marketing, credit risk evaluation, fraud detection and other predictive modelling applications from banking, financial services, direct marketing, insurance and telecommunications.

Prerequisite Skills

Before attending this course, you should have completed the SAS Essentials: An Introduction to SAS Programming course and the Applying Statistical Concepts using SAS course, or have equivalent experience.

SAS Modules Used

Base SAS and SAS/STAT.

Course Topics

Predictive Modelling

• business applications
• analytical challenges.

Fitting the Logistic Regression Model

• parameter estimation
• adjustments for over sampling.

Preparing the Input Variables

• dealing with missing values
• the problems of categorical inputs
• using variable clustering
• using subset selection.

Classifier Performance

• looking at ROC curves and lift charts
• calculating optimal cutoffs
• analysing K-S and c statistics.

Evaluating Many Models