Logistic Regression Workshop using R - Step by Step modeling
4.8 (16 ratings)
Instead of using a simple lifetime average, Udemy calculates a course's star rating by considering a number of different factors such as the number of ratings, the age of ratings, and the likelihood of fraudulent ratings.
55 students enrolled
Wishlisted Wishlist

Please confirm that you want to add Logistic Regression Workshop using R - Step by Step modeling to your Wishlist.

Add to Wishlist

Logistic Regression Workshop using R - Step by Step modeling

Learn R syntax for step by step logistic regression model development and validations
4.8 (16 ratings)
Instead of using a simple lifetime average, Udemy calculates a course's star rating by considering a number of different factors such as the number of ratings, the age of ratings, and the likelihood of fraudulent ratings.
55 students enrolled
Last updated 5/2017
English
Price: $20
30-Day Money-Back Guarantee
Includes:
  • 3 hours on-demand video
  • 6 Supplemental Resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
What Will I Learn?
  • Familiar with Syntax for - Step by step logistic regression modeling using R
View Curriculum
Requirements
  • Theory behind logistic regression - theory is not covered in this course
  • Familiarity with basic R syntax
Description

This course is a workshop on logistic regression using R. The course

  • Doesn't have much of theory - it is more of execution of R command for the purpose
  • Provides step by step process details
  • Step by step execution
  • Data files for the modeling
  • Excel file containing output of these steps

The content of the course is as follows

  1. Data Import and Data Sanity Check
  2. Development n Validation dataset preparartion  
  3. Important Categorical Variable selection 
  4. Important Numeric Variable Selection 
  5. Indicator Variable Creation 
  6. Stepwise Regression 
  7. Dealing with multicollinearity
  8. Logistic Regression Score n Probability generation in the data set
  9. Hands on KS Calculation
  10. Coefficient stability check
  11. Iterate for final model
Who is the target audience?
  • R professionals
  • Analytics Professionals
  • Data Scientists
Students Who Viewed This Course Also Viewed
Curriculum For This Course
29 Lectures
02:46:24
+
Course details and preparing yourself for modeling
3 Lectures 11:46

Detail of what is going to be covered in the course

Preview 01:49


Prepare R environment - Install R and R studio
03:48
+
Understand Data and go for variable selection
8 Lectures 53:52
Data Import n Sanity Check : Objective of the step
00:51

Data Import n Sanity Check - execution
10:16


Random partitioning : Development n Validation dataset preparartion - Execution
06:14

Important Categorical Variable selection - Step Objective
00:48

Important Categorical Variable selection - Execution
14:03

Important Numeric Variable Selection - Step Objective
01:23

+
Refine Variable list
6 Lectures 34:47

Indicator Variable Creation - execution
08:29

Stepwise Regression - step objective
00:54

Stepwise Regression - Execution
09:29

Dealing with multicollinearity - step objective
00:37

Dealing with multicollinearity - Execution
14:47
+
Iterate for final model
8 Lectures 39:58
Logistic Regression Score n Probability - step objective
00:46

Logistic Regression Score n Probability - Execution
07:29

KS Calculation - step objective
01:08

KS Calculation - Execution
13:36

Coefficient stability check - step objective
01:01


Iterate for final model - step objective
02:12

Iterate for final model - execution
08:57
+
Appendix topics - (Based on Student's demand) - will keep growing
4 Lectures 26:01

K fold Validation part 1 - divide data into k part (equal sized random split)
08:34

K fold Validation part 2 - Iterate k times and append the validation results
08:04

Closure Note
01:30
About the Instructor
Gopal Prasad Malakar
4.3 Average rating
1,594 Reviews
20,143 Students
16 Courses
Credit Card Analytics Professional - Trains on Data Mining

I am a seasoned Analytics professional with 16+ years of professional experience. I have industry experience of impactful and actionable analytics. I am a keen trainer, who believes that training is all about making users understand the concepts. If students remain confused after the training, the training is useless. I ensure that after my training, students (or partcipants) are crystal clear on how to use the learning in their business scenarios. My expertise is in Credit Card Business, Scoring (econometrics based model development), score management, loss forecasting and MS access based database application development.