| Enrich with Hyperscaler API |
Call the GCP API and enrich a modeling dataset that predicts customer churn. |
| GCP sentiment enrichment |
Demo the usage of the Google Cloud Natural Language API for sentiment analysis to enrich a customer churn dataset. |
| Churn problem framing |
Discover the problem framing and data management steps required to successfully model for churn, using a B2C retail example and a B2B example based on a DataRobot’s churn model. |
| Churn insights with Streamlit |
Use the Streamlit churn predictor app to present the drivers and predictions of your DataRobot model. |
| Synthetic training data |
Learn how to generate synthetic datasets that mimic real-world data for training, validation, and testing—enabling safe data sharing and model development when access to real data is limited due to privacy or regulatory constraints. |
| Feature engineering for molecular SMILES data |
Execute a feature engineering pipeline tailored for SMILES-formatted molecular data. |