SAS Enterprise Miner: Data Mining and Predictive Modeling

Name: SAS Enterprise Miner: Data Mining and Predictive Modeling
Rating: 4.3 (16 reviews)

Master predictive modeling and data mining using SAS Enterprise Miner.

Created byEDUCBA Bridging the Gap

Last updated 3/2024

English

What you'll learn

Introduction to SAS Enterprise Miner and its capabilities for predictive modeling and data mining.
Importing datasets in various formats such as text, CSV, xlsx, and xls.
Understanding user operating concepts and software menus within SAS Enterprise Miner.
Exploring statistical concepts like mean, standard deviation, and sample statistics.
Performing variable selection using techniques like input variables, R-square values, and binary target variables.
Combining different modeling techniques such as decision trees, neural networks, and regression models for enhanced predictive accuracy.
Building and evaluating neural network models, including model weight history, ROC charts, and iteration plots.
Implementing regression analysis with binary targets, interpreting regression model results, and creating effect plots.
Engaging in practical exercises, case studies, and interactive discussions to reinforce learning.

Course content

5 sections • 61 lectures • 9h 20m total length

Introduction of SAS Enterprise Miner11:35
Select a SAS Table9:54
Creating Input Data Node12:58
Create and configure the input data node in SAS Enterprise Miner, connect data sources, apply data partition and outlier filter, and view the generated SAS code behind the workflow.
Metadata Advisor Options8:53
Learn to build a time series dataset in SAS Enterprise Miner by configuring metadata fields, creating a transaction data source, and applying seasonal decomposition to reveal trends and seasonal components.
Add More Data Sources11:01
Sample Statistics10:22
Trial report9:32
Explore trial report workflows in SAS Enterprise Miner by creating scatter plots for binary and other variables, applying variable clustering, and examining correlation and cluster outputs and variable selection.
Properties of Cluster Node8:06
Explore the properties of the cluster node, examine input variables, standardization settings, Ward's minimum variance clustering, and interpret outputs like segment plots, cluster statistics, and mean values.
Variable Selection9:13
Learn how the principal components node derives new variables from original inputs via linear combinations, using eigenvectors of the correlation matrix and eigenvalues for dimensionality reduction in predictive modeling.

Input Variable9:38
Input Variable Continues9:43
Values of R-Square9:22
More on Variable Selection8:55
Binary Target Variable8:41
Variable and Effect Summary9:16
An overview of variable selection and binary target modeling in SAS Enterprise Miner, highlighting which variables are selected or rejected, their relative importance, and the built regression and nominal-scale analyses.
Variable Selection - Variable ID's8:39
Variable Frequency Table9:12
Variable S - Updating Model Comparison8:47
Run Data Partition Node8:14
Variable Selection - Fit Statistics9:22
Understanding Transformation of Variables9:37
Score Ranking Overlay Res9:08
Explore SAS Enterprise Miner regression modeling, focusing on variable selection before and after transformation, and evaluating lift, gain, and R2 to relate inputs to output variables.
Update Transformation of Variables9:46
Update and transform variables from partitioned data, apply optimal binning, and merge results into a regression model, while reviewing SAS code and model evaluation metrics.

Combination of Different Models8:44
Properties of Neural Network8:33
Analyzing the Output Variable12:16
Analyze a binary output variable using decision trees, neural networks, and regression in SAS Enterprise Miner, comparing training and validation results, scores, lifts, and generated SAS code.
Combination of Regression Model7:42
Combination - Result of Regression Node10:29
Combination Iteration Plot10:00
Subseries Plot11:55
Creating Densemble Diagram9:40
SAS Code11:55
Decision Tree Model10:06
Run and Upadate Decision Tree Model10:10
Learn to run and update a decision tree model in SAS Enterprise Miner, covering data partitioning, train validation test sets, output variables, and key fit statistics and visualizations.
Creating Dscore Node8:37
Apply the decision tree model to an independent prospect data set using a score node, predicting the probability of response and comparing models with a SAS code utility.
DT - Resulf of Model Comparison10:19
Leaf Statistics and Tree Map10:16
Interactively Decision Trees9:22
Explore interactive decision trees in SAS Enterprise Miner by building and modifying trees step by step, using a process flow with input, data partition, and three models for comparison.
Result Node Data Partition9:14
Interactively Trees Window9:06
Building a Decision Trees8:50

Neural Network Model10:10
Neural Network Model Output9:40
Model Weight History12:28
Explore how a neural network learns by tracking weights history across iterations, identifying optimal weights at the validation peak, and scoring new data with SAS Enterprise Miner.
Neural Network - Final Weight6:08
ROC Chart7:41
Neural Network -Iteration Plot8:45
Neural Network - SAS Code10:08
Neural Network - Cumulative Lift6:23
Decision Processing6:22
Results of Auto Neural Node7:01
Run Model Comparison8:00
DEX - Variable ID's10:48
Average Square Error6:14
Examine how switching to the average squared error function reshapes neural network results, revealing weight history, misclassification rates, and diagnostic plots like lift and mean squared error.
Score Rating overlay - Event5:41
Run Dmine Regression Node5:53
Learn how to run the mine regression node in SAS Enterprise Miner, review predicted values, fit statistics, and model comparisons, including ROC, lift, and profit measures.

Regression with Binary Target7:59
Regression - Table Effect Plots7:43
Result of Regression Model8:53
Run a regression node to model an ordinal target with three levels and a nominal loss frequency, examining training, validation, and test data and the SAS code.
Update Regression Node8:56
Update a regression node to view outputs, statistics, and plots in SAS Enterprise Miner, including target variables, residuals, training and validation data, and generated SAS code.
Creating Flow Diagram8:40

Requirements

Prior knowledge of Quantitative Methods, MS Office and Data will be useful

Description

Welcome to our course on SAS Enterprise Miner! In this comprehensive program, you will delve into the intricacies of predictive modeling and data mining using one of the industry's leading tools, SAS Enterprise Miner. Throughout this course, you will learn how to leverage the powerful features of SAS Enterprise Miner to extract meaningful insights from your data, build robust predictive models, and make informed business decisions. Whether you're a seasoned data analyst or a beginner in the field, this course will equip you with the skills and knowledge needed to excel in the world of data science and analytics using SAS Enterprise Miner. Join us on this exciting journey as we explore the vast capabilities of SAS Enterprise Miner and unlock the potential of your data!

Section 1: SAS Enterprise Miner Intro

In this section, you'll receive a comprehensive introduction to SAS Enterprise Miner, a powerful tool for predictive modeling and data mining. Starting with the basics, you'll learn how to navigate the interface, select datasets, and create input data nodes. Through hands-on demonstrations, you'll explore various features such as metadata advisor options, sample statistics, and trial reports, laying a strong foundation for your journey ahead.

Section 2: SAS Enterprise Miner Variable Selection

This section focuses on variable selection techniques in SAS Enterprise Miner. You'll delve into concepts like input variables, R-square values, and binary target variables. Through practical exercises, you'll gain insights into variable selection methods, frequency tables, and model comparison. By the end of this section, you'll be equipped with the skills to effectively choose and analyze variables for your predictive models.

Section 3: SAS Enterprise Miner Combination

In this section, you'll learn how to combine different models in SAS Enterprise Miner to enhance predictive accuracy. You'll explore techniques like decision trees, neural networks, and regression models. Through interactive sessions, you'll understand model iteration plots, subseries plots, and ensemble diagrams. By the end of this section, you'll be proficient in combining and analyzing diverse modeling techniques for optimal results.

Section 4: SAS Enterprise Miner Neural Network

This section delves into neural network modeling using SAS Enterprise Miner. You'll learn about neural network architectures, model weight history, and ROC charts. Through practical examples, you'll gain hands-on experience in building and evaluating neural network models. By mastering neural network techniques, you'll be able to tackle complex data mining tasks and extract valuable insights from your data.

Section 5: SAS Enterprise Miner Regression

In this final section, you'll explore regression modeling techniques in SAS Enterprise Miner. You'll learn how to perform regression analysis with binary targets, interpret regression model results, and create effect plots. Through step-by-step tutorials, you'll understand the intricacies of regression modeling and its applications in predictive analytics. By the end of this section, you'll have a solid understanding of regression techniques and their role in data-driven decision-making.

Throughout the course, you'll engage in practical exercises, real-world case studies, and interactive discussions to reinforce your learning. Whether you're a novice or an experienced data scientist, this course will empower you to harness the full potential of SAS Enterprise Miner for predictive modeling and data analysis.

Who this course is for:

Data analysts and scientists seeking to deepen their understanding of advanced analytics tools.
Business intelligence professionals aiming to leverage predictive modeling for decision-making.
Students and academics interested in learning practical applications of statistical modeling.
Professionals in industries such as finance, healthcare, marketing, and retail looking to apply predictive analytics to their domain-specific datasets.
Anyone keen on mastering techniques like variable selection, neural networks, regression analysis, and decision trees using SAS Enterprise Miner.

SAS Enterprise Miner: Data Mining and Predictive Modeling

What you'll learn

Explore related topics

Course content

SAS Enterprise Miner Intro9 lectures • 1hr 32min

SAS Enterprise Miner VARIABLE SELECTION14 lectures • 2hr 8min

SAS Enterprise Miner COMBINATION18 lectures • 2hr 57min

SAS Enterprise Miner NEURAL NETWORK15 lectures • 2hr 1min

SAS Enterprise Miner REGRESSION5 lectures • 42min

Requirements

Description

Who this course is for: