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Logistic Regression using SAS - Indepth Predictive Modeling

Analytics /Machine Learning / Data Science: Statistical / Econometrics foundation, SAS Program details, Modeling demo

Created byGopal Prasad Malakar

Last updated 11/2025

English

What you'll learn

Learn model development
Understand the science behind model development
Understand the SAS program required for various steps
Get comfortable with interpretation of SAS program output
See the step by step model development

Course content

10 sections • 111 lectures • 14h 16m total length

Course content6:31
Introduction to logistic Regression Modelling - High level8:34
Udemy Content details - Model workout details and excel file downloads3:32
Tips for Students2:55
Course Content PDF3:00

Section Outline3:59
Essential Data Quality3:45
Getting free access to SAS10:00
If by chance: you are uncomfortable with SAS?0:28
How to download excel / SAS code / word document etc.0:06
Feel the data - know it's contents9:02
Download Excel and word file for the explanation provided as part of feel the data section
Feel the data - View it's contents9:29
Feel the data - know it's distinct values9:02
Feel the data - know it's distribution13:27
Feel the data - Understand Coefficient of variance (need and applicability)8:16
Feel the data - know kurtosis and skewness4:47
Feel the data - know the percentile11:21
Feel the data - know stem n leaf diagram5:35
Feel the data - Understand box plot to detect outliers6:15
Feel the data - Understand and interpret normal probability plot22:27
Missing Value treatment And Flooring / Capping Guidiline13:49
Section FAQ- for variable treatment0:28
Check basic understanding of model design
Check basic understanding of data audit
Section PDF31:00

Section Outline3:00
Variable Selection - High level and flow chart of steps13:04
Important Character / Categorical Variable selection - high level6:07
Understand Chi-Square statistics for selecting Important Categorical Variables19:52
Please download the excel for better understanding!
Getting Chi-Square statistics using SAS8:36
Data Workout - Preamble11:02
Please download two excel and the word document for model / data work out
Model Workout - 01 Data Treatment34:52
Numeric Variable Selection - Part 0110:43
SAS Macro to check directional sense of numeric variable14:58
Dealing with Independent date variables (date variables as Xs)0:47
Recap Linear Regression4:08
Introduction to Logistic Regression11:57
Theory and Example of Step wise selection of Numeric Variable19:53
Appendix - Fisher's linear discriminant function to select important numeric Var9:23
Appendix - Information Value method of selecting important variables (all types)10:07
Appendix -Phi Square and Cramer's V for important categorical variable selection6:59
Section FAQ - for variable selection1:01
Section PDF1:04:00

Section Outline3:12
Common Sense Understanding of Multi collinearity and it's impact7:02
Detecting Multi Collinearity10:10
Multi Collinearity Treatment - part 0119:20
Multi Collinearity Treatment - part 025:58
Model Data workout - 02 Bi Variate strength of variables9:41
Model Data workout - 03 Multi Collinearity Treatment (Scientifically)12:36
FAQ for multi collinearity section1:13
Section PDF24:00

Section Outline3:05
Introduction to final model development steps4:07
Logistic Model Information - part 015:53
Logistic Model Information - part 024:48
Model Fit Statistics2:58
Log Likelihood15:12
Log Likelihood ratio - part 016:28
Log Likelihood Ratio - part 023:29
Model Fit Statistics - Revisit13:23
Maximum Likelihood Estimate12:44
Concordance, Somer's D, Gamma, Tau etc.18:47
Ideal logistic regression output4:17
Model Data Workout - part 04 Try Model on 10 variables6:50
Model Data Workout - part 05 Select best 8 variables9:26
Section PDF39:00

Section Outline2:21
Model Data Workout - part 06 Coefficient Stability Check11:28
Understand Score and Generate Score in the data set12:21
Theoretical Understanding of KS3:59
Model Data Workout - part 08 Generate KS Statistics for the model20:51
Model Data Workout - part 09 Understand and Generate Gini Statistics11:30
Model Data Workout - part 10 Understand & Apply Model Validation n Stability Chk7:56
FAQ - for strength of the model section0:58
Model Presentation Guideline - What should be presented to business5:00
Section PDF25:00
Other measures of Model Strength through confusion metrics3:04

Cross Validation Techniques (Holdout, K-Fold, Out of time, all but on etc.)7:53
K fold validation using simple SAS macro9:52
FAQ by students of this course (will keep growing overtime)13:15
Introduction to Multinomial Logistic Regression and solution approach10:06
Demo of multinomial logistic regression using SAS11:29
Ordinal Logistic Regression and Proportional Odds assumption8:14
About Data used for Ordinal Logistic Regression Demo3:56
Demo of Ordinal Logistic Regression using SAS10:37
Count Data Model - Poisson Regression0:35
Bonus Topic - how to learn Predictive Modeling / Logistic Regression with R1:06
Bonus Topic - Analytics / Data Science / Machine Learning Interview questions5:57
Credit Scoring (Parsimony Logistic Reg ) using a Zero-Code AI-Assisted Solution14:59
The video walks you through every stage of the model development lifecycle, completely removing the need for traditional coding:
Data Ingestion & Cleaning [01:59]: Easily load various file types (CSV, Excel, etc.) and see how the tool automatically handles data cleaning and variable generation, providing transparent SQL code for every step.
Comprehensive Data Audit [03:05]: Perform full univariate and bivariate analysis, including cross-tabulation, histograms, box plots, Q-Q plots, skewness, kurtosis, and the Shapiro-Wilk test [05:20].
Variable Analysis & Binning [06:02]: Visually analyze the trend of independent variables against the dependent variable (Target B). Create custom bins or use automated grouping features without writing code [06:53].
Automated Model Building [08:02]: Select Logistic Regression and watch the platform automatically perform crucial steps like variable selection, directional/sense checks, and multicollinearity treatment to arrive at a parsimonious model.
Robust Model Validation [10:17]: Review key validation metrics, including:
VIF, variable-wide coefficients, and significance.
KS Statistic and its automated interpretation [11:00].
Coefficient Stability Check and Population Stability Index (PSI) for data in and out of time [11:59].
Rich Visualization & Data Prep [12:36]: Explore built-in dynamic visualization tools like Tree Maps and Sunburst Charts with drill-down capabilities, alongside UI-based features for joining, appending, and aggregating data [13:33].
This video is your guide to leveraging AI assistance for faster, more accurate, and more transparent credit risk modeling.
Final Words1:59

Requirements

Basic knowledge of SAS

Description

What is this course all about?

This course is all about credit scoring / logistic regression model building using SAS. It explains

There course promises to explain concepts in a crystal clear manner. It goes through the practical issue faced by analyst. Some of the discussion item would be

How to clarify objective and ensure data sufficiency?
How do you decide the performance window?
How do you perform data treatment
How to go for variable selection? How to deal with numeric variables and character variables?
How do you treat multi collinerity scientifically?
How do you understand the strength of your model?
How do you validate your model?
How do you interpret SAS output and develop next SAS code accordingly?
Step by step workout - model development on an example data set

What kind of material is included?

It consists of video recording of screen (audio visual screen capture), pdf of presentations, Excel data for workout, word document containing code and Excel document containing step by step model development workout details

How long the course will take to complete?

Approximately 30 hours

How is the course structured?

It has seven sections, which step by step explains model development

Why Take this course?

The course is more intended towards students / analytics professionals to

Get crystal clear understanding
Get jobs in this kind of work by clearing interview with confidence
Be successful at their statistical or analytical profession due to the quality output they produce

Who this course is for:

Students
Analysts / Analytics professional
Modelers / Statisticians

Logistic Regression using SAS - Indepth Predictive Modeling

What you'll learn

Explore related topics

Course content

Course Outline5 lectures • 25min

Introduction to Credit Scoring / Credit Score card development9 lectures • 1hr 31min

Data Design for Modelling7 lectures • 1hr 19min

Data Audit - Make sure to check that data is right for the modelling18 lectures • 2hr 43min

Variable Selection - Select important numeric and character variables18 lectures • 4hr 10min

Multi Collinearity Treatment9 lectures • 1hr 33min

Iterate for final model / Understand strength of the model15 lectures • 2hr 30min

Strength of a Model and Model Validation Methods11 lectures • 1hr 44min

Reject Inference - Developing application score on scored population6 lectures • 36min

Appendix Topics (It will have contents based on student's demands)13 lectures • 1hr 40min

Requirements

Description

Who this course is for: