Predictive Modeling with Logistic Regression using SAS

Overview

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

Syllabus

Course Overview & Understanding Predictive Modeling

Welcome! In this module, you will review the fundamentals of predictive modeling. First we'll get you started by setting up the course environment. Then you explore the business scenario data that is used throughout the course. You’ll learn the goals of predictive modeling, key terms and model elements, and the basic workflow used to build predictive models, along with common real-world applications. You’ll also work through practical scenarios to explore data using descriptive statistics and frequency tables, and you’ll examine the code used to generate these summaries. Finally, you’ll learn about common data and analytical challenges.

Fitting the Model

In this module, you investigate the concepts behind the logistic regression model. Then you learn to use the LOGISTIC procedure to fit a logistic regression model. Finally, you learn how to score new cases and adjust the model for oversampling.

Preparing the Input Variables, Part 1

In this module, you learn how to deal with common problems with your predictor variables such as missing values, categorical predictors with many levels, a high number of redundant predictors, and nonlinear relationships with the response variable.

Preparing the Input Variables, Part 2

In this module, you learn how to select the most predictive variables to use in your model.

Measuring Model Performance

In this module, you learn how to assess the performance of your model and how to determine allocation rules that maximize profit. Finally, you learn how to generate a family of increasingly complex predictive models and how to select the best model.