Logistic Regression (Predictive Modeling) workshop using R
What you'll learn
- Familiar with Syntax for - Step by step logistic regression modeling using R
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
- Data Import and Data Sanity Check
- Development n Validation dataset preparartion
- Important Categorical Variable selection
- Important Numeric Variable Selection
- Indicator Variable Creation
- Stepwise Regression
- Dealing with multicollinearity
- Logistic Regression Score n Probability generation in the data set
- Hands on KS Calculation
- Coefficient stability check
- Iterate for final model
Who this course is for:
- R professionals
- Analytics Professionals
- Data Scientists
Instructor
- 4.4 Instructor Rating
- 11,554 Reviews
- 115,377 Students
- 18 Courses
I am a seasoned Analytics professional with 22+ years of professional experience. I have industry experience of impactful and actionable analytics, data science, decision strategy and enterprise wise data strategy.
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, business intelligence systems like tableau /SAS Visual Analytics, MS access based database application development, Enterprise wide big data framework and streaming analysis.
Please refer to my course for
- Data analysis and Data science using Python
- SAS / R program details (syntax and options)
- SAS / R output deep dive
- Practical usage in Industrial situation