Welcome to my series on getting started with Python integration to SAS Viya for predictive modeling.
- Exploring Data - Learn how to explore the data before fitting a model
- Working with Dates - Learn how to format a SAS Date and calculate a new column
- Imputing Missing Values - Learn how to replace missing values
- Creating Training and Validation Data Sets - Learn a way to split your data into a training and validation data set to be used for modeling
- Fitting a Linear Regression - Learn how to fit a linear regression and use your model to score new data
- Fitting a Logistic Regression - Learn how to fit a logistic regression and use your model to score new data
- Fitting a Decision Tree - Learn how to fit a decision tree and use your decision tree model to score new data
- Comparing Logistic Regression and Decision Tree - Which of our models is better at predicting our outcome? Learn how to compare models using misclassification, area under the curve (ROC) charts, and lift charts with validation data.
- Fitting a Random Forest - Learn how to fit a random forest and use your model to score new data
- Fitting a Gradient Boosting Model - Learn how to fit a gradient boosting model and use your model to score new data
- Autotuning Your Model - Learn how to improve your models by utilizing the built-in autotuning feature in SAS Viya
- Creating new Features Automatically - Learn how to utilize SAS to automatically create features for your models
- Creating Machine Learning Pipelines Automatically - Learn how to use AI within SAS Viya to create machine learning pipelines that start with the data, create new features, fit several models, and select the best model overall.
- Ask the Expert Webinar - How Do I Use Open Source with SAS® Viya®? - Check out my open source with SAS Viya one-hour webinar.
- Ask the Expert Webinar - How Do I Use Python in SAS® Viya®? - Check out the Python integration in SAS Viya one-hour webinar!
- CAS Action! - a series on fundamentals blog series - Here is a series of posts centered on CAS Actions. In this series the CAS language (CASL) is used to call CAS actions, however, actions can also be specified with the Python SWAT package to process data in the distributed CAS server with small changes to the syntax for the Python language.