Grow your data science skills at posit::conf(2024)

August 12th-14th in Seattle

Predicting Lending Rates with Databricks, tidymodels, and Posit Team

Machine learning algorithms are reshaping financial decision-making, changing how the industry manages financial risk.

Garrett Grolemund at Posit shows how to use both Posit and Databricks to apply machine learning methods to the consumer credit market, where accurately predicting lending rates is critical for customer acquisition.

*Please note that while the workflow focuses on a financial example, the general workflow will be useful to those using Databricks and R together across any industry.

During this workflow demo, you will learn how to:

  1. Connect to historical lending rate data stored in Databricks Delta Lake
  2. Tune and cross-validate a penalized linear regression (LASSO) that predicts interest rates
  3. Select variables with the penalized linear regression model (LASSO)
  4. Build an interactive Shiny app to provide a customer-facing user interface for our model
  5. Deploy the app to production on Posit Connect, and arrange for the app to access Databricks

Resources for the demo:

Additional follow-up links:

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