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
- 4.3 Instructor Rating
- 12,951 Reviews
- 120,839 Students
- 18 Courses
I am a seasoned analytics professional and the CEO of ProData Science AI Consultancy Pvt Ltd, with over 24 years of professional experience. I specialize in impactful and actionable analytics, data science, decision strategy, and enterprise-wide data strategy.
ProData Science offers outstanding solutions for building machine learning models. These solutions make the model-building process enjoyable, accurate, and efficient.
Additionally, I am a dedicated trainer who believes that effective training ensures users understand the concepts thoroughly. If students remain confused after the training, it is ineffective. I ensure that my students or participants are crystal clear on how to apply their learning in real business scenarios.
My expertise includes:
-- Credit card business
-- Scoring ( Machine learning / econometrics-based model development)
-- Score management
-- Loss forecasting / Time series forecasting
-- Business intelligence solutions such as Tableau, SAS Visual Analytics, Microsoft Power BI etc.
-- Enterprise-wide big data framework and streaming analysis
Please refer to my course for
- Data analysis and Data science using SAS / R / Python
- SAS / R / Python program details (syntax and options)
- SAS / R / Python output deep dive
- Practical usage in Industrial situation