# Data enrichment and preparation

> Data enrichment and preparation - Data enrichment and preparation accelerators that you can add to
> your experiment workflow.

This Markdown file sits beside the HTML page at the same path (with a `.md` suffix). It summarizes the topic and lists links for tools and LLM context.

Companion generated at `2026-05-06T18:17:09.576347+00:00` (UTC).

## Primary page

- [Data enrichment and preparation](https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/data-enrichment-prep/index.html): Full documentation for this topic (HTML).

## Related documentation

- [Developer documentation](https://docs.datarobot.com/en/docs/api/index.html): Linked from this page.
- [Developer learning](https://docs.datarobot.com/en/docs/api/dev-learning/index.html): Linked from this page.
- [AI accelerators](https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/index.html): Linked from this page.
- [Enrich with Hyperscaler API](https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/data-enrichment-prep/enrich-hyper.html): Linked from this page.
- [GCP sentiment enrichment](https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/data-enrichment-prep/gcp-enrich.html): Linked from this page.
- [Churn problem framing](https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/data-enrichment-prep/ml-churn.html): Linked from this page.
- [Churn insights with Streamlit](https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/data-enrichment-prep/streamlit-app.html): Linked from this page.
- [Synthetic training data](https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/data-enrichment-prep/synth-data.html): Linked from this page.
- [Feature engineering for molecular SMILES data](https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/data-enrichment-prep/smiles.html): Linked from this page.

## Documentation content

| Topic | Description |
| --- | --- |
| 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. |