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

What you will 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,

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.


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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

English
language

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