Logistic Regression (Credit Scoring) Modeling using SAS [FIRSTTHINKER]

Logistic Regression (Credit Scoring) Modeling using SAS

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

Instructed by Gopal Prasad Malakar Business / Data & Analytics

Course 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
What are the requirements?

Basic knowledge of SAS
What am I going to get from this course?

Over 85 lectures and 16 hours of content!
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
What is the target audience?

Students
Analysts / Analytics professional
Modelers / Statisticians
Curriculum

Section 1: Course Outline
Lecture 1
Course content
Preview
06:31
Lecture 2
Introduction to logistic Regression Modelling - High level
Preview
08:34
Lecture 3
Udemy Content details - Model workout details and excel file downloads
Preview
03:32
Lecture 4
Tips for Students
02:55
Lecture 5
Course Content PDF
3 pages
Section 2: Introduction to Credit Scoring / Credit Score card development
Lecture 6
Section outline
Preview
01:23
Lecture 7
3C Concept of Credit Approval Process
15:31
Lecture 8
High Level Understanding of Score
09:31
Lecture 9
Benefit of scoring (modelling)
20:25
Lecture 10
Introduction to modeling
07:09
Lecture 11
Types of scores
Preview
12:47
Lecture 12
A typical risk score
04:16
Lecture 13
Section PDF
20 pages
Section 3: Data Design for Modelling
Lecture 14
Section outline
Preview
02:44
Lecture 15
Model Design Example
17:54
Lecture 16
Model Design - definitions and pointers
13:19
Lecture 17
Decide Performance window by Vintage Analysis
Preview
14:51
Lecture 18
Model Design Precaution
08:18
Lecture 19
Section PDF
20 pages
Section 4: Data Audit - Make sure to check that data is right for the modelling
Lecture 20
Section Outline
Preview
03:59
Lecture 21
Essential Data Quality
03:45
Lecture 22
How to download excel / word files ?
Preview
02:39
Lecture 23
Feel the data - know it's contents
09:02
Lecture 24
Feel the data - View it's contents
09:29
Lecture 25
Feel the data - know it's distinct values
09:02
Lecture 26
Feel the data - know it's distribution
13:27
Lecture 27
Feel the data - Understand Coefficient of variance (need and applicability)
08:16
Lecture 28
Feel the data - know kurtosis and skewness
04:47
Lecture 29
Feel the data - know the percentile
11:21
Lecture 30
Feel the data - know stem n leaf diagram
Preview
05:35
Lecture 31
Feel the data - Understand box plot to detect outliers
06:15
Lecture 32
Feel the data - Understand and interpret normal probability plot
22:27
Lecture 33
Missing Value treatment And Flooring / Capping Guidiline
13:49
Lecture 34
Section PDF
31 pages
Section 5: Variable Selection - Select important numeric and character variables
Lecture 35
Section Outline
Preview
03:00
Lecture 36
Variable Selection - High level and flow chart of steps
13:04
Lecture 37
Important Character / Categorical Variable selection - high level
Preview
06:07
Lecture 38
Understand Chi-Square statistics for selecting Important Categorical Variables
19:52
Lecture 39
Getting Chi-Square statistics using SAS
08:36
Lecture 40
Data Workout - Preamble
11:02
Lecture 41
Model Workout - 01 Data Treatment
34:52
Lecture 42
Numeric Variable Selection - Part 01
10:43
Lecture 43
SAS Macro to check directional sense of numeric variable
14:58
Lecture 44
Recap Linear Regression
04:08
Lecture 45
Introduction to Logistic Regression
11:57
Lecture 46
Theory and Example of Step wise selection of Numeric Variable
19:53
Lecture 47
Appendix - Fisher's linear discriminant function to select important numeric Var
09:23
Lecture 48
Appendix - Information Value method of selecting important variables (all types)
10:07
Lecture 49
Appendix -Phi Square and Cramer's V for important categorical variable selection
06:59
Lecture 50
Section PDF
64 pages
Section 6: Multi Collinearity Treatment
Lecture 51
Section Outline
Preview
03:12
Lecture 52
Common Sense Understanding of Multi collinearity and it's impact
07:02
Lecture 53
Detecting Multi Collinearity
10:10
Lecture 54
Multi Collinearity Treatment - part 01
19:20
Lecture 55
Multi Collinearity Treatment - part 02
05:58
Lecture 56
Model Data workout - 02 Bi Variate strength of variables
09:41
Lecture 57
Model Data workout - 03 Multi Collinearity Treatment (Scientifically)
12:36
Lecture 58
Section PDF
24 pages
Section 7: Iterate for final model / Understand strength of the model
Lecture 59
Section Outline
Preview
03:05
Lecture 60
Introduction to final model development steps
04:07
Lecture 61
Logistic Model Information - part 01
05:53
Lecture 62
Logistic Model Information - part 02
04:48
Lecture 63
Model Fit Statistics
02:58
Lecture 64
Log Likelihood
15:12
Lecture 65
Log Likelihood ratio - part 01
06:28
Lecture 66
Log Likelihood Ratio - part 02
03:29
Lecture 67
Model Fit Statistics - Revisit
13:23
Lecture 68
Maximum Likelihood Estimate
12:44
Lecture 69
Concordance, Somer's D, Gamma, Tau etc.
Preview
18:47
Lecture 70
Ideal logistic regression output
04:17
Lecture 71
Model Data Workout - part 04 Try Model on 10 variables
06:50
Lecture 72
Model Data Workout - part 05 Select best 8 variables
09:26
Lecture 73
Section PDF
39 pages
Section 8: Strength of a Model and Model Validation Methods
Lecture 74
Section Outline
Preview
02:21
Lecture 75
Model Data Workout - part 06 Coefficient Stability Check
11:28
Lecture 76
Understand Score and Generate Score in the data set
12:21
Lecture 77
Theoretical Understanding of KS
Preview
03:59
Lecture 78
Model Data Workout - part 08 Generate KS Statistics for the model
20:51
Lecture 79
Model Data Workout - part 09 Understand and Generate Gini Statistics
11:30
Lecture 80
Model Data Workout - part 10 Understand & Apply Model Validation n Stability Chk
07:56
Lecture 81
Model Presentation Guideline - What should be presented to business
05:00
Lecture 82
Final Words
01:59
Lecture 83
Section PDF
25 pages
Lecture 84
How to download excel / word files ?
2 pages
Lecture 85
FAQ by students of this course (will keep growing overtime)
Text
Instructor Biography


Gopal Prasad Malakar , Credit Card Analytics Professional - Trains on Data Mining
I am a seasoned Analytics professional with 14+ years of professional experience. I have industry experience of impactful and actionable analytics. I am a keen trainer, who believes that training is all about making users understand the concepts. If students remain confused after the training, the training is useless. I ensure that after my training, students (or partcipants) are crystal clear on how to use the learning in their business scenarios. My expertise is in Credit Card Business, Scoring (econometrics based model development), score management, loss forecasting and MS access based database application development.