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SAS Tutorial | Machine Learning: A Coding Example in SAS

In this SAS How To Tutorial, Christa Cody shows how to create machine learning models using SAS Viya. First, you will be shown a coding example in SAS of supervised learning in SAS Studio which is great for those who are familiar with SAS programming. She will then create the same model using Model Studio machine learning pipelines. Christa’s approach may be more useful for someone not interested in programming the model themselves, but who still wants to have control over hyperparameters.

After watching this video and see the coding examples, you will have a better understanding of how to build machine learning models in SAS. If you are interested Machine Learning Fundamentals or why Christa is doing certain machine learning tasks, check out this SAS Tutorial | Machine Learning Fundamentals video (https://youtu.be/8k2MbSljPes).

Download Data Files
Download the HMEQ data set that Christa uses http://support.sas.com/documentation/...

Content Outline
00:40 – Example: Supervised Learning in SAS Studio
* Setting variables
* Load the data
* Explore missing values
* Partition data
* Impute values
* Identify variables that explain variance
* Build predictive model using random forest
* Score the model
* Access the model using Lift and ROC information
* Analyze the model

12:55 – Example: Model Studio Machine Learning Pipelines
* Create new project and import data
* Data tab
* Pipelines tab
* Run pipeline
* Imputation results
* Add Random Forest
* Autotune Feature
* View results
* Assessment tab with ROC and Lift plots

Learn more about SAS Software
Blog: Learn more about Christa and machine learning – https://blogs.sas.com/content/sgf/201...
How-to tutorial: Machine Learning Fundamentals – https://youtu.be/8k2MbSljPes
Book: Big Data, Data Mining, and Machine Learning: Value Creation for Business Leaders and Practitioners – https://www.sas.com/store/books/categ...
$79 e-Learning course: Machine Learning on SAS® Viya® – https://support.sas.com/edu/schedules...


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Video - 24 minutes