Add AudiencePro, statguard, and StreamXL

[Mullassery]

New libraries

Three Rust-powered Python libraries for data engineering and ML:

Data Processing

• statguard — Declarative data quality and validation library with a contract DSL compiled to a columnar execution plan. Schema checks, drift detection (PSI + KS), anomaly detection, and native Delta Lake/Iceberg/Parquet support. 13–25× faster than pandera and Great Expectations.
• StreamXL — Streaming XLSX reader that processes large Excel files row-by-row at constant memory. ~27k rows/sec, 68 MB at 250k rows vs openpyxl's 911 MB. 4–5× faster overall.

Machine Learning & AI

• AudiencePro — Customer segmentation library combining RFM analysis, KMeans/K-Prototypes clustering, drift detection, and streaming incremental updates in one package. 10–25× faster than the scikit-learn + pandas pipeline.

All three use PyO3 bindings, are pip/uv installable, and support Python 3.8+.

[gemini-code-assist]

Code Review

This pull request adds three new libraries (statguard, StreamXL, and AudiencePro) to the README.md file. The review feedback points out stylistic inconsistencies regarding the use of em-dashes in the descriptions of statguard and AudiencePro, suggesting alternative phrasing to align with the rest of the document.

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[gemini-code-assist]

The use of the em-dash (—) with spaces is inconsistent with the rest of the README.md file, which uses standard hyphens, colons, or prepositions for punctuation. Consider rephrasing to use a preposition like 'supporting' to maintain stylistic consistency across the list.

Suggested change

	- [statguard](https://github.com/Mullassery/statguard) - Declarative data quality and validation library — schema checks, drift detection (PSI + KS), anomaly detection, and native Delta Lake/Iceberg support. 13–25× faster than pandera and Great Expectations.
	- [statguard](https://github.com/Mullassery/statguard) - Declarative data quality and validation library supporting schema checks, drift detection (PSI + KS), anomaly detection, and native Delta Lake/Iceberg support. 13–25× faster than pandera and Great Expectations.

[gemini-code-assist]

The use of the em-dash (—) with spaces is inconsistent with the rest of the README.md file, which uses standard hyphens, colons, or prepositions for punctuation. Consider rephrasing to use a preposition like 'featuring' to maintain stylistic consistency across the list.

Suggested change

	- [AudiencePro](https://github.com/Mullassery/AudiencePro) - Python library for customer segmentation — RFM analysis, KMeans/K-Prototypes clustering, drift detection, and streaming updates at 10–25× the speed of scikit-learn + pandas.
	- [AudiencePro](https://github.com/Mullassery/AudiencePro) - Python library for customer segmentation featuring RFM analysis, KMeans/K-Prototypes clustering, drift detection, and streaming updates at 10–25× the speed of scikit-learn + pandas.