Machine Learning using the sasviya.ml Python Package | SAS Viya Workbench Quick Start Tutorial
This tutorial is designed for Python programmers who want to build and evaluate machine learning models using in SAS Viya Workbench using the optimized models available in the sasviya.ml package. You explore basic data preprocessing tasks required for machine learning and then build linear and nonlinear machine learning models to predict a binary target. In addition to best practices, you learn how to compare and evaluate model performance to select a champion model.
Download the data
The SAS Viya Workbench Quick Start Repository provides access to the notebook used in this demonstration – https://github.com/sascommunities/sas...
Chapters
00:00 – Introduction
01:16 – Import the necessary packages and download the data
01:07 – Download the data
03:28 – Explore the data
05:48 – Partition the data
09:10 – Impute missing values
12:06 – Select useful inputs
14:14 – sasviya.ml integration with scikit-learn
16:28 – Logistic regression
18:02 – Decision tree
18:57 – Random forest
20:06 – Gradient Boosting
21:03 – Support Vector Machine
22:01 – Score and assess the models
26:35 – Deploy the models
Additional Resources
◉ SAS Viya Workbench – https://www.sas.com/en_us/software/vi...
◉ SAS Viya Workbench Documentation – https://go.documentation.sas.com/doc/...
◉ SAS Learning Subscription – https://www.sas.com/en_us/training/pr...
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