This course covers predictive modeling using SAS/STAT software with emphasis on the LOGISTIC procedure. This course also discusses selecting variables and interactions, recoding categorical variables based on the smooth weight of evidence, assessing models, treating missing values and using efficiency techniques for massive data sets.

Aprenda a

• use logistic regression to model an individual's behavior as a function of known inputs
• create effect plots and odds ratio plots using ODS Statistical Graphics
• handle missing data values
• tackle multicollinearity in your predictors
• assess model performance and compare models.

A quién va dirigido

Modelers, analysts and statisticians who need to build predictive models, particularly models from the banking, financial services, direct marketing, insurance and telecommunications industries

Formatos disponibles	Duración estándar (la duración puede variar, consulte su horario)
Presencial:	2.0 días
Live Web:	4 sesión(es) de medio-día
e-Learning:	16 horas/180 día

Before attending this course, you should

• have experience executing SAS programs and creating SAS data sets, which you can gain from the Programación SAS 2: Gestión de Datos course
• have experience building statistical models using SAS software
• have completed a statistics course that covers linear regression and logistic regression, such as the Estadística 1: Introducción a ANOVA, Regresión y Regresión Logística course.

Este curso utiliza SAS/STAT software.

Predictive Modeling

• business applications
• analytical challenges

Fitting the Model

• parameter estimation
• adjustments for oversampling

Preparing the Input Variables

• missing values
• categorical inputs
• variable clustering
• variable screening
• subset selection

Classifier Performance

• ROC curves and Lift charts
• optimal cutoffs
• K-S statistic
• c statistic
• profit
• evaluating a series of models