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Predictive Modeling Using Logistic Regression

Course code: PMLR51

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.
1 200 EUR

1 452 EUR including VAT

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Course dates

Starting date: Upon request

Type: Upon request

Course duration: 14 hours

Language: en

Price without VAT: 1 200 EUR

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Starting
date		 Place
Type Course
duration		 Language Price without VAT
Upon request Upon request 14 hours en 1 200 EUR Register
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Target group

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

Course structure

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
  • Profit.
  • Evaluating a series of models.

Prerequisites

Before attending this course, you should:
  • Have experience executing SAS programs and creating SAS data sets, which you can gain from the SAS Programmierung 2: Datenmanagement course.
  • Have experience building statistical models using SAS software.
  • Have completed a statistics course that covers linear regression and logistic regression, such as the Statistics 1: Introduction to ANOVA, Regression, and Logistic Regression course.

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