Enterprise Guide : Logistic Regression (IND83)
Enterprise Guide : Logistic Regression (IND83)
Course Duration: 3Days
This course covers the skills required to assemble analysis flow diagrams using the rich tool set of SAS Enterprise Miner for both pattern discovery (segmentation, association, and sequence analyses) and predictive modeling (decision tree, regression, and neural network models).
Specifically, you should be able to:
- define a SAS Enterprise Miner 5 project and explore data graphically
- modify data for better analysis results
- build and understand predictive models like decision trees and regression models
- compare and explain complex models
- generate and use score code
- apply association and sequence discovery to transaction data
- use other modeling tools like rule induction, gradiant boosting, and support vector machines.
Introduction
o Touring SAS Enterprise Miner 5.3
o Placing SAS Enterprise Miner in the analysis workflow
o Application examples and case studies
o Pattern discovery and predictive modeling
Accessing and Assaying Prepared Data
o Defining a SAS Enterprise Miner 5.3 project
o Defining a data source
o Validating source data
Introduction to Predictive Modeling with Decision Trees
o Defining a modeling data source
o Partitioning data for model development
o Growing a decision tree with the Desktop Tree Application
o Running the Decision Tree node
o Using Decision Tree node options
o Understanding predictive modeling results data
Introduction to Predictive Modeling with Regressions
o Comparing linear and logistic regression
o Using the Regression node
o Imputing missing values with the Impute node
o Replacing data values with the Replacement node
o Performing input selection
o Understanding regression modeling output
o Extending regression models with polynomial and interaction terms
Audience
Before attending this course, you should be acquainted with Microsoft Windows and Windows-based software. In addition, you should have at least an introductory-level familiarity with basic statistics and regression modeling. Previous SAS software experience is helpful but not necessary.
Prerequisites
This course covers the skills required to assemble analysis flow diagrams using the rich tool set of SAS Enterprise Miner for both pattern discovery (segmentation, association, and sequence analyses) and predictive modeling (decision tree, regression, and neural network models).
- Online
- Onsite
- Classroom
- Customized
- Assessment
Student Testimonials
Ultramax Infonet provides rigorous technical and practical training on SAS . In addition to training it also conducts TNA’s which prepares students for GLOBAL Certification Exams. Training provided for Base and Advance SAS was fantastic for most of the modules.
Rishab Bothra
Swati Jadhav
Neetu Sharma
It has been a great experience at Ultramax infonet for me, right from class room to placement.
I am immensely thankful to Ultramax infonet pune for providing SAS training program which helped me groom and prepare myself for the challenges in the industries. The Faculty and staff are all dedicated to student success and innovation. The flexible and professional environment for learning provides the opportunity to explore yourself
Palash Sahare
Shripad Binawade
Google referred me to approach “Ultramax” and I was glad to connect with them on proper instruction my training for three modules Base, Advance SAS and Predictive Modeling were successfully completed on time. The staff is very kind and informative. Proper time for practicing was provided which helped in clearing our doubts. My experience can be rated as 4.8/5. Proper guidance. Good practice labs and sessions for doubt solving. Ultramax, did help we get a nice job
Tanvi Yedke
I wanted to learn Base, Advance SAS and Predictive Modeling. It was the best institute to learn and get trained from here. They provide with all best facilities, classrooms are very well maintained and equipped. Trainer for base and advance was very good. Concepts were very clear and trainers were also professional.Overall experience was very well. It provided by quite good opportunities for placement
Kalyani Uttarwar
Harsha Kamat
