[DRAFT] guide: integrating chDB, Cloud and Scikit learn

[Blargian]

Summary

Adds a guide which details how you can use Cloud, chDB and Scikit learn together to train a model and run inference.

Covers:

• How to query data from Cloud using chDB with Arrow for efficient transfer
• How chDB can be used to easily switch back and forth between familiar DataFrames and processing in ClickHouse
• How to train a binary classifier on a subset of the UK property price datasets (predict property type = flat or property type = detached house)
• How to use that model in chDB to run inference
• How to use that model with ClickHouse using UDFs

To do:

• How to run inference in Cloud (executable UDFs not yet GA)

Checklist

• Delete items not relevant to your PR
• URL changes should add a redirect to the old URL via https://github.com/ClickHouse/clickhouse-docs/blob/main/docusaurus.config.js
• If adding a new integration page, also add an entry to the integrations list here: https://github.com/ClickHouse/clickhouse-docs/blob/main/docs/integrations/index.mdx

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Project	Deployment	Preview	Updated (UTC)
clickhouse-docs	Ready	Preview	Nov 5, 2025 9:42am

[dhtclk]

Suggested change

	:::note [TL;DR]
	This guide demonstrates how chDB complements scikit-learn for ML workflows by building a binary classifier that predicts UK property types. You'll learn how to:
	- Use chDB for fast feature engineering on 11.8M records from ClickHouse Cloud
	- Build and train a Random Forest classifier achieving ~87% accuracy
	- Deploy the model back to ClickHouse via UDFs for real-time inference
	The pattern shown here applies to any binary classification problem where you need efficient data preprocessing at scale.
	Time required: 45-60 minutes
	:::