SAS Predictive Modeling

SAS Predictive Modeling

SAS Predictive Modeling

Understand various statistical concepts and using SAS Enterprise Miner in predicting data.

Language: english

Note: 0/5 (0 notes) 612 students

Instructor(s): EDUCBA Bridging the Gap

Last update: 2022-07-14

What you’ll learn

  • Understand the worth of this course of predictive modeling with SAS enterprise miner.
  • Skills like skill to analyze data and see a complex pattern, coding skill, and strong understanding of concepts.
  • This course teaches the individual to be comfortable with coding so that it can be industry ready and work in the practical world.
  • understand the course modules such as regression, classification, neural network, pm with SAS EM variables selection,

 

Requirements

  • Some of the prerequisites for this predictive modeling training course must be fulfilled otherwise the understanding of this course could become difficult for some trainees to understand, the prerequisite of this course is not very complicated, the basics requirEMents for the course are a basic understanding of statistics is required. If you have forgotten the simple terms of statistics, you may have revision before the training modules. One must have knowledge about ms excel as you will be using excel data in SAS enterprise miner therefore some understanding of ms excel is required. An introduction to a programming language is necessary so that an individual can work easily with coding .if the requirEMents are not matched then one should go for a bridge course and they can apply for this predictive modeling course.

 

Description

Predictive modeling is the process of studying the data models. To predict models a different set of methods of statistics are used .these models are made by numerous predictors. SAS enterprise miner tends to provide us with several tools for predictive modeling. the methods used in predictive modeling comes from several areas of research, including statistics, pattern recognition, and machine learning. By this course you will be able to have complete knowledge of predictive modeling with SAS enterprise miner. The trainee will study the research of different predictors and predict data according to different concepts. It is currently the most commonly used in computer science, information technology, and information services domain.

This course covers many skills that students can add up for jobs and careers. These skills are explained here to help students understand the worth of this course of predictive modeling with SAS enterprise miner. Skills like skill to analyze data and see a complex pattern, coding skill, and strong understanding of concepts. It is very necessary to understand data when you are willing or working for predictive modeling and make sense in no time and this is taught in this course theoretically as well as practically with examples. This course teaches the individual to be comfortable with coding so that it can be industry ready and work in the practical world. It also helps in a strong understanding of the concepts of the course modules such as regression, classification, neural network, pm with SAS EM variables selection, predictive modeling with SAS EM basics and more concepts that are taught which are frequently asked in interviews and which will judge an individual’s understanding about the predictive modeling with SAS enterprise miner.

It aims to provide knowledge to the trainees about SAS enterprise miner and how it can be used in predictive modeling. This training program will help the trainee to master all the concepts of SAS enterprise miner and after the end of this course, trainees will be able to work with this programming language.

The objective of the predictive modeling training program is to assist people who are willing to learn from scratch. The trainee will be able to use data mining processes to create highly accurate predictive and descriptive models and will provide predictive modeling skills as mentioned by business sectors/domains. After the end of this program trainees will be able to work effectively and efficiently and he will be through with all the concepts of SAS enterprise miner

 

Who this course is for

  • This course is suitable for an ample range of students .this needs to be checked to make things clear in your mind if predictive modeling with SAS enterprise miner is appropriate for you or not. The students from mechanical or computer science fields from mathematics or statistics background are suitable. The working professionals from the software field, banking, insurance, share market, information technologies who want to drift to data analysis are more suitable and they comprise a major crowd of our class size. This predictive modeling with the SAS enterprise miner course is also suitable for managers and experienced industry professionals who want to further promote thEMselves as data scientists. People from various fields also take this predictive modeling with SAS enterprise miner training to perform data analysis in their particular fields. Most commonly students having graduate degrees and master’s or postgraduates can apply easily for this course.

 

Course content

  • SAS Predictive Modeling 01 – Introduction
    • 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
  • SAS Predictive Modeling 02 – Variables
    • Input Variable
    • Values of R-Square
    • 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
  • SAS Predictive Modeling 04 – Neural Networks
    • 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 Domine Regression Node
  • SAS Predictive Modeling 05 – Regression
    • Regression with Binary Target
    • Regression – Table Effect Plots
    • Result of Regression Model
    • Update Regression Node
    • Creating Flow Diagram
  • Logistic Regression Project using SAS Stat
    • Introduction to Logistic Regression Project using SAS Stat
    • Insurance Dataset Explanation and Exploration
    • Logistic Regression Demonstration Part 1
    • Logistic Regression Demonstration Part 2
    • Missing Values Imputation
    • Categorical Inputs
    • Categorical Inputs Continue
    • Variable Clustering Part 1
    • Variable Clustering Part 2
    • Variable Clustering Part 3
    • Variable Screening
    • Variable Screening Continue
    • Logit Plots
    • Subset Selection Part 1
    • Subset Selection Part 2
    • Subset Selection Part 3
    • Subset Selection Part 4
    • Subset Selection Part 5
    • Subset Selection Part 6
    • Subset Selection Part 7
    • Subset Selection Part 8

 

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