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- Course content
- Introduction to logistic Regression Modelling - High level
- Udemy Content details - Model workout details and excel file downloads
- Tips for Students
- Course Content PDF
Quick Facts
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Medium of instructions
English
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Mode of learning
Self study
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Mode of Delivery
Video and Text Based
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Course and certificate fees
Fees information
₹ 3,099
certificate availability
Yes
certificate providing authority
Udemy
The syllabus
Course Outline
Introduction to Credit Scoring / Credit Score card development
- Section outline
- 3C Concept of Credit Approval Process
- High Level Understanding of Score
- Benefit of scoring (modelling)
- Introduction to modeling
- Types of scores
- A typical risk score
- Introduction to Scoring FAQ
- Section PDF
Data Design for Modelling
- Section outline
- Model Design Example
- Model Design - definitions and pointers
- Decide Performance window by Vintage Analysis
- Model Design Precaution
- FAQ : for model design section
- Section PDF
Data Audit - Make sure to check that data is right for the modelling
- Section Outline
- Essential Data Quality
- Getting free access to SAS
- If by chance: you are uncomfortable with SAS?
- How to download excel / SAS code / word document etc.
- Feel the data - know it's contents
- Feel the data - View it's contents
- Feel the data - know it's distinct values
- Feel the data - know it's distribution
- Feel the data - Understand Coefficient of variance (need and applicability)
- Feel the data - know kurtosis and skewness
- Feel the data - know the percentile
- Feel the data - know stem n leaf diagram
- Feel the data - Understand box plot to detect outliers
- Feel the data - Understand and interpret normal probability plot
- Missing Value treatment And Flooring / Capping Guidiline
- Section FAQ- for variable treatment
- Check basic understanding of model design
- Check basic understanding of data audit
- Section PDF
Variable Selection - Select important numeric and character variables
- Section Outline
- Variable Selection - High level and flow chart of steps
- Important Character / Categorical Variable selection - high level
- Understand Chi-Square statistics for selecting Important Categorical Variables
- Getting Chi-Square statistics using SAS
- Data Workout - Preamble
- Model Workout - 01 Data Treatment
- Numeric Variable Selection - Part 01
- SAS Macro to check directional sense of numeric variable
- Dealing with Independent date variables (date variables as Xs)
- Recap Linear Regression
- Introduction to Logistic Regression
- Theory and Example of Step wise selection of Numeric Variable
- Appendix - Fisher's linear discriminant function to select important numeric Var
- Appendix - Information Value method of selecting important variables (all types)
- Appendix -Phi Square and Cramer's V for important categorical variable selection
- Section FAQ - for variable selection
- Section PDF
Multi Collinearity Treatment
- Section Outline
- Common Sense Understanding of Multi collinearity and it's impact
- Detecting Multi Collinearity
- Multi Collinearity Treatment - part 01
- Multi Collinearity Treatment - part 02
- Model Data workout - 02 Bi Variate strength of variables
- Model Data workout - 03 Multi Collinearity Treatment (Scientifically)
- FAQ for multi collinearity section
- Section PDF
Iterate for final model / Understand strength of the model
- Section Outline
- Introduction to final model development steps
- Logistic Model Information - part 01
- Logistic Model Information - part 02
- Model Fit Statistics
- Log Likelihood
- Log Likelihood ratio - part 01
- Log Likelihood Ratio - part 02
- Model Fit Statistics - Revisit
- Maximum Likelihood Estimate
- Concordance, Somer's D, Gamma, Tau etc.
- Ideal logistic regression output
- Model Data Workout - part 04 Try Model on 10 variables
- Model Data Workout - part 05 Select best 8 variables
- Section PDF
Strength of a Model and Model Validation Methods
- Section Outline
- Model Data Workout - part 06 Coefficient Stability Check
- Understand Score and Generate Score in the data set
- Theoretical Understanding of KS
- Model Data Workout - part 08 Generate KS Statistics for the model
- Model Data Workout - part 09 Understand and Generate Gini Statistics
- Model Data Workout - part 10 Understand & Apply Model Validation n Stability Chk
- FAQ - for strength of the model section
- Model Presentation Guideline - What should be presented to business
- Section PDF
Reject Inference - Developing application score on scored population
- Section Overview
- Introduction to reject inference! What it is? Why it is needed?
- How to do reject inference?
- Impact of the new model - swapset analysis / more base with same approval rate
- Swapset analysis supplementary video
- Do you need reject inference all the time?
Appendix Topics (It will have contents based on student's demands)
- Cross Validation Techniques (Holdout, K-Fold, Out of time, all but on etc.)
- K fold validation using simple SAS macro
- FAQ by students of this course (will keep growing overtime)
- Introduction to Multinomial Logistic Regression and solution approach
- Demo of multinomial logistic regression using SAS
- Ordinal Logistic Regression and Proportional Odds assumption
- About Data used for Ordinal Logistic Regression Demo
- Demo of Ordinal Logistic Regression using SAS
- Count Data Model - Poisson Regression
- Bonus Topic - how to learn Predictive Modeling / Logistic Regression with R
- Bonus Topic - Analytics / Data Science / Machine Learning Interview questions
- Final Words
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