Course Outline
  • 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
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