Learn logistic regression modeling in 1 week

A hands-on, project-based training to help you quickly learn predictive modeling skills through real-world project work.
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  • Lectures 26
  • Length 2.5 hours
  • Skill Level All Levels
  • Languages English
  • Includes Lifetime access
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    Available on iOS and Android
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About This Course

Published 1/2013 English

Course Description

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.

What are the requirements?

  • Microsoft Excel and Powerpoint
  • SAS Software with Base/Stat/Graph Components

What am I going to get from this course?

  • Learn how to develop a logistic regression model from the beginning to the end
  • Learn how to interpret modeling results
  • Learn how to present modeling results to others

Who is the target audience?

  • Working professionals who want to learn predictive modeling skills
  • Graduates or undergraduates who want to explore their career opportunities in advanced analytics

What you get with this course?

Not for you? No problem.
30 day money back guarantee.

Forever yours.
Lifetime access.

Learn on the go.
Desktop, iOS and Android.

Get rewarded.
Certificate of completion.


Section 1: Introduction
In this section, we will explain why predictive analytics is an important skill, what you will learn from this class, what is so unique about this training class. We will also provide course outlines and prerequisites for taking this class.
Section 2: Overview

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.

Section 3: Project

In this section, we will provide modeling project overview. This session will cover:

  • Business background, business objective and the challenges.
  • Data to used in the project
  • High-level analytical solution
  • Quick summary of key learning

Web link for data and document download for the project:


Project Overview - Part 2
Section 4: Data

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.

Section 5: Methodology
In this section, we will provide high-level overview of modeling methodology. First, we will describe two different types of logistic regression model. Then we will discuss modeling equation for binary logistic regression. Next we will explain how variables and their coefficients are being estimated using statistical approach. We will end the section with a summary.
Section 6: Modeling

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.

Step-by-step demo on how to load a text file into SAS
Step-by-step demo on how to apply labels to SAS variables
Step-by-step demo on how to performance data checking and create QC report
Modeling Step 1C - Data QC - Part 2
Step-by-step demo on how to do data transformation
Modeling Step 1D - Data Transformation - Part 2
In this section, you will learn how to develop / use SAS tool to select top 50 to 100 most important variables out of up-to-hundreds of potential predictors. This helps to focus our efforts on important variables for further deep dive analysis.
The objective for this section is for you to learn how to use a SAS visual tool to discover the relationship pattern between predictors and target variable. We will use this relationship pattern to help us transform variable in next section.
Modeling Step 3 - Deep Dive Analysis - Part 2

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.

Modeling Step 4 - Transform Variables - Part 2
Modeling Step 4 - Transform Variables - Part 3
In this section, you will learn how to develop logistic regression model, how to run model diagnostics, how to create model scoring equation, how to score whole modeling data, and how to create model performance chart. At the end of the class, you will be able to develop a logistics regression model independently.
Modeling Step 5 - Develop Model - Part 2
Modeling Step 5 - Develop Model - Part 3
In this section, you will learn how to prepare validation data, how to score model on validation data, and how to validate model performance. At the end of the class, you will be able to evaluate model performance on validation sample.
Modeling Step 6 - Validate Model - Part 2
Section 7: Results
In this section, you will learn how to prepare model results slides. We will provide examples on how to write up 3 key components for presentation: Executive Summary, Modeling Approach, and Modeling Results. At the end of the section, you will be able to write up presentation materials for a model.
Section 8: Appendix. Downloadable SAS Codes Used in the Section 6
SAS Code Used in Modeling Steps 1-6

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Instructor Biography

Predictive Analytics, Advanced Analytics Made Simple

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.

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