Logistic Regression (Predictive Modeling) workshop using R

Course Curriculum

Chapter 1: Course details and preparing yourself for modeling

Lecture 1: Course details

Lecture 2: Curriculum details – How does the course progress?

Lecture 3: Prepare R environment – Install R and R studio

Chapter 2: Understand Data and go for variable selection

Lecture 1: Data Import n Sanity Check : Objective of the step

Lecture 2: Data Import n Sanity Check – execution

Lecture 3: Random partitioning : Development n Validation dataset preparartion – Objective

Lecture 4: Random partitioning : Development n Validation dataset preparartion – Execution

Lecture 5: Important Categorical Variable selection – Step Objective

Lecture 6: Important Categorical Variable selection – Execution

Lecture 7: Important Numeric Variable Selection – Step Objective

Lecture 8: Important Numeric Variable Selection – Excution

Chapter 3: Refine Variable list

Lecture 1: Indicator Variable Creation – step objective

Lecture 2: Indicator Variable Creation – execution

Lecture 3: Stepwise Regression – step objective

Lecture 4: Stepwise Regression – Execution

Lecture 5: Dealing with multicollinearity – step objective

Lecture 6: Dealing with multicollinearity – Execution

Lecture 7: Section FAQ – for variable selection

Chapter 4: Iterate for final model

Lecture 1: Logistic Regression Score n Probability – step objective

Lecture 2: Logistic Regression Score n Probability – Execution

Lecture 3: KS Calculation – step objective

Lecture 4: KS Calculation – Execution

Lecture 5: Coefficient stability check – step objective

Lecture 6: Coefficient stability check – Execution

Lecture 7: Iterate for final model – step objective

Lecture 8: Iterate for final model – execution

Chapter 5: Appendix topics – (Based on Student's demand) – will keep growing

Lecture 1: Cross Validation (Hold out sample, OOT validation, K fold validation etc.)

Lecture 2: K fold Validation part 1 – divide data into k part (equal sized random split)

Lecture 3: K fold Validation part 2 – Iterate k times and append the validation results

Lecture 4: FAQ on K fold cross validation

Lecture 5: Introduction to Multinomial Logistic Regression

Lecture 6: Demo of Multinomial Logistic Regression using R

Lecture 7: Ordinal Logistic Regression and Proportional Odds assumption

Lecture 8: About Data used for Ordinal Logistic Regression Demo

Lecture 9: Demo of Ordinal Logistic Regression using R

Lecture 10: Count Data Model – Poisson Regression

Lecture 11: Closure Note