In this quickstart, we learned how to create ML models using Teradata In-Database Analytic Functions. We created our own database, td_analytics_functions_demo, and loaded the customer, accounts, and transactions data from the val database.
We performed feature engineering using TD_OneHotEncodingFit, TD_ScaleFit, and TD_ColumnTransformer. We then used TD_TrainTestSplit to split the dataset into training and testing tables. Next, we trained a model using TD_GLM, scored the testing dataset using TD_GLMPredict, and evaluated the scored results using TD_RegressionEvaluator.