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. Autotuning Your Model - Learn how to improve your models by utilizing the built in autotuning feature in SAS Viya
12. Creating new Features Automatically - Learn how to utilize SAS to automatically create your features for your models
13. Creating Machine Learning Pipelines Automatically - Learn how use AI withing SAS Viya to create machine learning pipelines that start with the data, create new features, fit several models, and select the best model overall.

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