Logistic Regression Modelling (Credit Scoring) using SAS -step by step
Theory, SAS program explanation, SAS output deep dive & interpretation and Model data workout steps
The course promises to explain concepts in a crystal clear manner. It goes through the practical issue faced by analyst.
- 72 mp4 HD Videos
- 4 Excel File
- 2 Word Document
- 8 PDF Files
Some of the discussion items would be:
- How to clarify objective and ensure data sufficiency.
- How to go for variable selection? How to deal with numeric variables and character variables.
- What is the approach to take when you starting with 1000 variables vs. when you are starting with 150 variables.
- Understanding multi collinearity removal steps and recommendations.
- Understanding step wise regression
- Understanding different section of logistic regression output
- Understand model validation and coefficient stability check
- Understanding the strength of the model - KS, Gini
Section 1 – Introduction to Model
- Understand what is a model? What is a credit score? What is modelling?
- Where it is used?
- What benefit it brings?
- Discuss various kinds of score / models.
- What makes a typical scorecard
Section 2 – Data design
- Steps of model building
- Understand, terms associated with modelling like historical window, performance period etc.
- Understand, how to select performance window
- Understand about the characteristics, which can be considered, for model building.
- Practical scenarios associated with model design discussion
Section 3 - Data Audit and Treatment
- How to build data for model building
- How to apply exclusions before getting into the data
- Learn data investigation techniques through simple but powerful techniques. The trick lies in getting into the more details and finer prints of the output.
- Proc contents
- Proc print
- Proc means
- Proc freq
- Examining bi-variate plot
Section 4 – Variable selection
- How to go for variable selection
- Different variable selection techniques such as
- Chi Square
- Stepwise Regression
- Info value
- Fishers linear discriminant function
- Cramer's V, Phi Square
- Guidance about, which technique to apply when.
Section 5 – Multi collinearity Treatment
- What is multi collinearity?
- Recommendations about how remove it?
- Understand output of each steps of multi collinearity removal.
- Understand terms associated with the same
- Factor loading
- Wald chi square (bi variate and multi-variate)
Section 6 – Iterate for final model development
- How to apply forward / backward stepwise regression
- How to decide about final number of variables in the model
- Deep dive in logistic regression output
- Understanding terms associated with logistic regression output
- Log likelihood ratio
- Somer's D
Section 7 – Validate model
- Rank ordering
- Gini Statistics
- Coefficient stability
- Model usage guideline
- Model presentation guideline
logistic, logistic regression, logistic regression using SAS, credit scoring, SAS, scoring, modelling, data mining, video course
Language of instruction: English