SAS Predictive Modeling – Predictive Analytics and Modeling

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Learn how to build Predictive Models using Numerical Data using tools such as SAS

What you will learn

It covers topics such as PM SAS EM Introduction, PM SAS EM variable selection, SAS PM EM combination, SAS PM EM neural network, and SAS PM EM regression.

It starts with an introduction to SAS and then gradually move towards topics such as selecting SAS tables, creating input data nodes

decision tree in SAS, creating score model, ROC chart, Neural network training, regression -table effect to name a few.

we provide more details on the predictive modeling course content and explain at a very high-level what concepts will be covered under course

Description

Predictive modeling can be understood as the process of creation, test, and validation of a model. It uses concepts from statistics in predicting the outcomes. Predictive modeling contains a different set of methods like machine learning, statistics, artificial intelligence and so on. These models are made up of several predictors, also called attributes that are likely to impact future results. Predictive modeling is currently the most widely used in computer science, information technology, and information services domain.

This predictive modeling course targets to provide predictive modeling skills as mentioned above to business sectors/domains. Quantitative methods and predictive modeling concepts from this predictive modeling course could be extensively used in many fields to understand the current customer behavior, customer satisfaction, financial market trends, studying effects of medicine in pharma sectors after drugs are developed and administered.

Minitab or SAS and SPSS are among the leading developers in the world towards building statistical analysis software. Across the world, these software’s are used by thousands of companies. These are also used by over 10000 universities and colleges for research and teaching. Some major clients of Minitab, for example, consist of Pfizer, Royal Bank of Scotland, Nestle, Boeing, Toshiba, and DuPont.


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Many independent studies conducted by companies like Mckinsey, Gartner, and others have predicted that data science, machine learning, and predictive modeling is going to be the biggest jobs of the 21st century and these professionals are going to be rewarded the best for it.

This course covers many tangible skills that students can count on for jobs and career switch. These skills are explained here to help students understand the value of this predictive modeling course.

  • Skill to analyze data and see a complex pattern: data understanding and pattern extraction is a key skill for predictive modeling and a successful person in this domain should be able to make sense of data in no time. In this course, you will learn how to do that. You will be taught various types of data distribution, data patterns, and data understanding techniques. These skills will help you lifelong in making better and more intuitive decisions in all fields of work.
  • Hands-on coding skill: – The predictive modeling course teaches three tools- Minitab, SAS, and SPSS. For that, this predictive modeling course is quite good. For predictive modeling and machine learning course one needs to be comfortable with coding, and hence having a sharp understanding of practical implementation is very important. This course teaches all these skills so that the student is industry ready and can comfortably work in real-life use cases.
  • Strong understanding of concepts: – Machine learning concepts such as regression, classification, support vector machines, neural network, ROC curve, and many more concepts are taught which are frequently asked in interviews and which judges a candidate’s understanding of predictive modeling.
English
language

Content

SAS – Predictive Modeling with SAS Enterprise Miner

Introduction of SAS Enterprise Miner
Select a SAS Table
Creating Input Data Node
Metadata Advisor Options
Add More Data Sources
Sample Statistics
Trial report
Properties of Cluster Node
Variable Selection
Input Variable
Input Variable Continues
Values of R-Square
More on Variable Selection
Binary Target Variable
Variable and Effect Summary
Variable Selection – Variable ID’s
Variable Frequency Table
Variable S – Updating Model Comparison
Run Data Partition Node
Variable Selection – Fit Statistics
Understanding Transformation of Variables
Score Ranking Overlay Res
Update Transformation of Variables
Combination of Different Models
Properties of Neural Network
Analyzing the Output Variable
Combination of Regression Model
Combination – Result of Regression Node
Combination Iteration Plot
Subseries Plot
Creating Densemble Diagram
SAS Code
Decision Tree Model
Run and Upadate Decision Tree Model
Creating Dscore Node
DT – Resulf of Model Comparison
Leaf Statistics and Tree Map
Interactively Decision Trees
Result Node Data Partition
Interactively Trees Window
Building a Decision Trees
Neural Network Model
Neural Network Model Output
Model Weight History
Neural Network – Final Weight
ROC Chart
Neural Network -Iteration Plot
Neural Network – SAS Code
Neural Network – Cumulative Lift
Decision Processing
Results of Auto Neural Node
Run Model Comparison
DEX – Variable ID’s
Average Square Error
Score Rating overlay – Event
Run Dmine Regression Node
Regression with Binary Target
Regression – Table Effect Plots
Result of Regression Model
Update Regression Node
Creating Flow Diagram
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