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Logistic Regression using SAS - Indepth Predictive Modeling

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Logistic Regression using SAS - Indepth Predictive Modeling

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

4.3

(1148)

6556 enrolled on this course

Last updated 11/2021

Gopal Prasad Malakar

₹ 2999.0

₹ 4499.0

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30-Day Money-Back Guarantee

Lessons

Quizzes

Duration

13 Hours

Skill level

Beginner

Language

English

Certificate

Yes

Full lifetime access

Yes

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

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

Requirements

You will need a copy of Adobe XD 2019 or above. A free trial can be downloaded from Adobe.
No previous design experience is needed.
No previous Adobe XD skills are needed.

Course Content

27 sections • 95 lectures

Expand All Sections

1-Course Outline

1.1-Course content

1.2-Introduction to logistic Regression Modelling - High level

1.3-Udemy Content details - Model workout details and excel file downloads

1.4-Tips for Students

1.5-Course Content PDF

2-Introduction to Credit Scoring / Credit Score card development

2.1-Section outline

2.2-3C Concept of Credit Approval Process

2.3-High Level Understanding of Score

2.4-Benefit of scoring (modelling)

2.5-Introduction to modeling

2.6-Types of scores

2.7-A typical risk score

2.8-Introduction to Scoring FAQ

2.9-Section PDF

2.10-Check your learning of Section 2 content

3-Data Design for Modelling

3.1-Section outline

3.2-Model Design Example

3.3-Model Design - definitions and pointers

3.4-Decide Performance window by Vintage Analysis

3.5-Model Design Precaution

3.6-FAQ : for model design section

3.7-Section PDF

3.8-Check your understanding of section 3

4-Data Audit - Make sure to check that data is right for the modelling

4.1-Section Outline

4.2-Essential Data Quality

4.3-Getting free access to SAS

4.4-If by chance: you are uncomfortable with SAS?

4.5-How to download excel / SAS code / word document etc.

4.6-Feel the data - know it's contents

4.7-Feel the data - View it's contents

4.8-Feel the data - know it's distinct values

4.9-Feel the data - know it's distribution

4.10-Feel the data - Understand Coefficient of variance (need and applicability)

4.11-Feel the data - know kurtosis and skewness

4.12-Feel the data - know the percentile

4.13-Feel the data - know stem n leaf diagram

4.14-Feel the data - Understand box plot to detect outliers

4.15-Feel the data - Understand and interpret normal probability plot

4.16-Missing Value treatment And Flooring / Capping Guidiline

4.17-Section FAQ- for variable treatment

4.18-Check basic understanding of model design

4.19-Check basic understanding of data audit

4.20-Section PDF

5-Variable Selection - Select important numeric and character variables

5.1-Section Outline

5.2-Variable Selection - High level and flow chart of steps

5.3-Important Character / Categorical Variable selection - high level

5.4-Understand Chi-Square statistics for selecting Important Categorical Variables

5.5-Getting Chi-Square statistics using SAS

5.6-Data Workout - Preamble

5.7-Model Workout - 01 Data Treatment

5.8-Numeric Variable Selection - Part 01

5.9-SAS Macro to check directional sense of numeric variable

5.10-Dealing with Independent date variables (date variables as Xs)

5.11-Recap Linear Regression

5.12-Introduction to Logistic Regression

5.13-Theory and Example of Step wise selection of Numeric Variable

5.14-Appendix - Fisher's linear discriminant function to select important numeric Var

5.15-Appendix - Information Value method of selecting important variables (all types)

5.16-Appendix -Phi Square and Cramer's V for important categorical variable selection

5.17-Section FAQ - for variable selection

5.18-Section PDF

6-Multi Collinearity Treatment

6.1-Section Outline

6.2-Common Sense Understanding of Multi collinearity and it's impact

6.3-Detecting Multi Collinearity

6.4-Multi Collinearity Treatment - part 01

6.5-Multi Collinearity Treatment - part 02

6.6-Model Data workout - 02 Bi Variate strength of variables

6.7-Model Data workout - 03 Multi Collinearity Treatment (Scientifically)

6.8-FAQ for multi collinearity section

6.9-Section PDF

7-Iterate for final model / Understand strength of the model

7.1-Section Outline

7.2-Introduction to final model development steps

7.3-Logistic Model Information - part 01

7.4-Logistic Model Information - part 02

7.5-Model Fit Statistics

7.6-Log Likelihood

7.7-Log Likelihood ratio - part 01

7.8-Log Likelihood Ratio - part 02

7.9-Model Fit Statistics - Revisit

7.10-Maximum Likelihood Estimate

7.11-Concordance, Somer's D, Gamma, Tau etc.

7.12-Ideal logistic regression output

7.13-Model Data Workout - part 04 Try Model on 10 variables

7.14-Model Data Workout - part 05 Select best 8 variables

7.15-Section PDF

8-Strength of a Model and Model Validation Methods

8.1-Section Outline

8.2-Model Data Workout - part 06 Coefficient Stability Check

8.3-Understand Score and Generate Score in the data set

8.4-Theoretical Understanding of KS

8.5-Model Data Workout - part 08 Generate KS Statistics for the model

8.6-Model Data Workout - part 09 Understand and Generate Gini Statistics

8.7-Model Data Workout - part 10 Understand & Apply Model Validation n Stability Chk

8.8-FAQ - for strength of the model section

8.9-Model Presentation Guideline - What should be presented to business

8.10-Section PDF

8.11-Other measures of Model Strength through confusion metrics

9-Reject Inference - Developing application score on scored population

9.1-Section Overview

9.2-Introduction to reject inference! What it is? Why it is needed?

9.3-How to do reject inference?

9.4-Impact of the new model - swapset analysis / more base with same approval rate

9.5-Swapset analysis supplementary video

9.6-Do you need reject inference all the time?

10-Appendix Topics (It will have contents based on student's demands)

10.1-Cross Validation Techniques (Holdout, K-Fold, Out of time, all but on etc.)

10.2-K fold validation using simple SAS macro

10.3-FAQ by students of this course (will keep growing overtime)

10.4-Introduction to Multinomial Logistic Regression and solution approach

10.5-Demo of multinomial logistic regression using SAS

10.6-Ordinal Logistic Regression and Proportional Odds assumption

10.7-About Data used for Ordinal Logistic Regression Demo

10.8-Demo of Ordinal Logistic Regression using SAS

10.9-Count Data Model - Poisson Regression

10.10-Bonus Topic - how to learn Predictive Modeling / Logistic Regression with R

10.11-Bonus Topic - Analytics / Data Science / Machine Learning Interview questions

10.12-Final Words

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