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Predictive Modeling Using Logistic Regression
This course covers predictive modeling using SAS/STAT software with emphasis on the LOGISTIC procedure.
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Course Description
About Program
Format of Training
Prerequisite
Training Features
Course Curriculum
Course Fees
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ContactCourse Description
Modelers, analysts and statisticians who need to build predictive models, particularly models from the banking, financial services, direct marketing, insurance and telecommunications industries
About Program
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.
Format of Training
Live Web Classroom
Training is Led by an expert instructor live online using live web training platform, and this are not pre recorded lectures. Trainer can virtually look over your shoulder. Discuss, share, exchange ideas with students from different countries.
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Prerequisite
Before attending this course, you should
- have experience executing SAS programs and creating SAS data sets, which you can gain from the SAS Programming 2: Data Manipulation Techniques 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.
This course addresses SAS/STAT software.
Training Features
- 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.
Course Curriculum
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
Course Fees
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