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SAS predictive modeling, especially useful if you are in banking, insurance, and healthcare as these industries love SAS

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SAS Predictive Modeling Using Logistic Regression

 

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What you’ll learn
  • Understand the challenges of analytics.
  • Gain an intuitive understanding of Logistic Regression.
  • Perform a data audit.
  • Perform a Uni-variate and Bi-variate analysis of features/variables.
  • Deal with Missing Data. (Multiple Imputation).
  • Adjustments for Oversampling.
  • Dimension Reduction for your Categorical Inputs.
  • Subset selection (utilizing automatic techniques for variable/feature selection).
  • Analyze Multicollinearity in your Features.
  • Analyze Classifier Performance (ROC curves, Optimal cutoffs, K-S).
  • Partition your Data, Train a Model, and Make Predictions on Unseen Data.

 

 

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Requirements
  • If you have no experience with SAS Base programming or beginner statistics, I would suggest first gaining that knowledge.
 
Description

Prerequisite:  ‘Learn SAS and Become a Data Ninja’ or equivalent. While I discuss the SAS code associated with performing logistic regression, I don’t do it in any real detail.

If you are in the banking/finance, healthcare, insurance industry, chances are the programming language of choice for your company is SAS.

 

Remember, you have a 30 day money back guarantee in case you do not enjoy the course! But I am confident you will enjoy it.

The base of the course is complete but you can expect additions of content throughout 2019.

Sign up today and learn SAS Predictive Modeling.

 

Who this course is for:
  • Companies who are interested in training their workforce in the different aspects of predictive modeling, including ultimately training a model and making predictions on unseen data. (Banking industry, insurance, etc)
  • Companies who want their workforce to familiarize themselves with the power of SAS programming.
SAS predictive modeling, especially useful if you are in banking, insurance, and healthcare as these industries love SAS
 
 
SAS Predictive Modeling Using Logistic Regression

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