Getting Started with Python Integration to SAS Viya for Predictive Modeling - Index


Welcome to my series on getting started with Python integration to SAS Viya for predictive modeling.

  1. Exploring Data - Learn how to explore the data before fitting a model
  2. Working with Dates - Learn how to format a SAS Date and calculate a new column
  3. Imputing Missing Values - Learn how to replace missing values
  4. 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
  5. Fitting a Linear Regression - Learn how to fit a linear regression and use your model to score new data
  6. Fitting a Logistic Regression - Learn how to fit a logistic regression and use your model to score new data
  7. Fitting a Decision Tree - Learn how to fit a decision tree and use your decision tree model to score new data
  8. 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.
  9. Fitting a Random Forest - Learn how to fit a random forest and use your model to score new data
  10. Fitting a Gradient Boosting Model - Learn how to fit a gradient boosting model and use your model to score new data
  11. Fitting a Neural Network model - Learn how to fit a neural network model and use your model to score new data
  12. Fitting a Support Vector Machine (SVM) Model - learn how to fit a support vector machine (SVM) model and use your model to score new data
  13. Saving the Best Model - Learn how to save the best model to use for future scoring activities
  14. Creating new Features Automatically - Learn how to utilize SAS to automatically create features for your models
  15. Autotuning Your Model - Learn how to improve your models by utilizing the built-in autotuning feature in SAS Viya
  16. 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.

Additional Resources



About Author

Melodie Rush

Principal Data Scientist, SAS Customer Success Technical Team

Melodie Rush is a Principal Data Scientist for the Customer Success Technical Team at SAS Institute. Melodie received both her B.S. in Statistics and her Masters in Science of Management from North Carolina State University. Since joining SAS, Melodie has developed presentations and methodology for doing many types of analysis, including data mining, forecasting, data exploration and visualization, quality control and marketing. She has spent more than 20 years helping companies identify and solve problems in each of these analytical areas.

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