This is a hands-on training class for those of you who want to learn or improve predictive modeling skills. We will focus on developing and validating a logistic regression model in this class.
We will walk you through our 6-step modeling process step-by-step. Together, we will solve a real-world modeling project with real-world data. We will provide extensive demo on SAS code development, running SAS codes, and explaining SAS outputs. We will also provide you all the SAS codes used in this course.
At the end of the class, you should be able to develop a logistic regression model independently.
In this section, you will learn what a logistic regression model is, why it is an important tool for decision scientists, and how it is being used in different industries, to make smarter decisions and create better outcomes. At the end of the section, we will do a quick summary of what we have learned.
In this section, we will provide modeling project overview. This session will cover:
Web link for data and document download for the project:
In this section, we will review modeling data. We will start with reviewing modeling samples and overall data summary. Then we will discuss key variables in the data. We will take a quick look at data, followed by an overview on predictors and data dictionary. We will end the section with key takeaways.
In this section, we will first walk you through our 6-step modeling process. Then we will dive into each of the modeling steps. In each step, we will do extensive demo to show you how to use or develop SAS program. We will run the SAS program and provide explanation on SAS outputs. For you to get the best out of this section, we recommend that you open your SAS program and try to execute the same program along with us.
In this section, you will learn how to use bivariate plots for variable transformations, how to impute missing values, how to handle extreme values, and how to perform non-linear transformations. At the end of the class, you will be able to perform sophisticated variable transformations.
15+ years of professional experience on predictive modeling in different industries. Specialized in logistic regression, linear regression, generalized regression, survival analysis, decision tree, discriminant analysis, cluster analysis, and other advanced analytic. In-depth knowledge and experiences in customer acquisition, retention, and credit risk management. Developed training classes and taught classes to graduate students and other working professionals.