audit: sweep Machine Learning, three-way split, drop h2o, mindsdb, scikit-lego, TabGAN, spark.ml

Restructure: the 12-entry flat section splits into General
(scikit-learn 234.7M/mo obvious choice; pgmpy 843.7K/mo and
feature-engine — renamed from feature_engine to its canonical PyPI
name, 297.1K/mo — challengers), Gradient Boosting (xgboost 52M/mo,
lightgbm 26.5M/mo, catboost 6.3M/mo, all obvious choices; lightgbm's
lightgbm-org link verified current — microsoft/LightGBM redirects
there), and Time Series Forecasting (timesfm sole — a foundation
model judged by ecosystem adoption, 285K/mo and 27.6K stars; prophet
and darts are named absences, deliberately not added this sitting).

Removed:
- h2o — 215.1K downloads/month, 7.5K stars, and the repo is active;
  the drop is purely editorial: no longer anyone's unprompted answer
  against scikit-learn and the boosting trio. Judgment call.
- mindsdb — the linked repo redirects to mindsdb/mindshub, a "models
  workspace"; the AI-layer-for-databases product this entry described
  no longer exists (verified). 23.9K downloads/month.
- scikit-lego — 72.5K downloads/month, 1.4K stars; a grab-bag of
  sklearn extras that never became an unprompted answer. Judgment.
- TabGAN — 574 stars, 2.3K downloads/month. Nowhere near the bar.
- spark.ml — duplicate in all but name: pyspark is already listed in
  the audited DevOps group, same repo, same pip install. Structural.

Co-Authored-By: Claude <noreply@anthropic.com>

README.md

@@ -191,18 +191,16 @@ _Frameworks for Neural Networks and Deep Learning. Also see [awesome-deep-learni
 
 _Libraries for Machine Learning. Also see [awesome-machine-learning](https://github.com/josephmisiti/awesome-machine-learning#python)._
 
-- [catboost](https://github.com/catboost/catboost) - A fast, scalable, high performance gradient boosting on decision trees library.
-- [feature_engine](https://github.com/feature-engine/feature_engine) - sklearn compatible API with the widest toolset for feature engineering and selection.
-- [h2o](https://github.com/h2oai/h2o-3) - Open Source Fast Scalable Machine Learning Platform.
-- [lightgbm](https://github.com/lightgbm-org/LightGBM) - A fast, distributed, high performance gradient boosting framework.
-- [mindsdb](https://github.com/mindsdb/minds) - MindsDB is an open source AI layer for existing databases that allows you to effortlessly develop, train and deploy state-of-the-art machine learning models using standard queries.
-- [pgmpy](https://github.com/pgmpy/pgmpy) - A Python library for probabilistic graphical models and Bayesian networks.
-- [scikit-learn](https://github.com/scikit-learn/scikit-learn) - The most popular Python library for Machine Learning with extensive documentation and community support.
-- [scikit-lego](https://github.com/koaning/scikit-lego) - A collection of lego bricks for scikit-learn pipelines.
-- [spark.ml](https://github.com/apache/spark) - [Apache Spark](https://spark.apache.org/)'s scalable [Machine Learning library](https://spark.apache.org/docs/latest/ml-guide.html) for distributed computing.
-- [TabGAN](https://github.com/Diyago/Tabular-data-generation) - Synthetic tabular data generation using GANs, Diffusion Models, and LLMs.
-- [timesfm](https://github.com/google-research/timesfm) - A pretrained foundation model from Google Research for time-series forecasting.
-- [xgboost](https://github.com/dmlc/xgboost) - A scalable, portable, and distributed gradient boosting library.
+- General
+  - [scikit-learn](https://github.com/scikit-learn/scikit-learn) - The most popular Python library for Machine Learning with extensive documentation and community support.
+  - [pgmpy](https://github.com/pgmpy/pgmpy) - A Python library for probabilistic graphical models and Bayesian networks.
+  - [feature-engine](https://github.com/feature-engine/feature_engine) - sklearn compatible API with the widest toolset for feature engineering and selection.
+- Gradient Boosting
+  - [xgboost](https://github.com/dmlc/xgboost) - A scalable, portable, and distributed gradient boosting library.
+  - [lightgbm](https://github.com/lightgbm-org/LightGBM) - A fast, distributed, high performance gradient boosting framework.
+  - [catboost](https://github.com/catboost/catboost) - A fast, scalable, high performance gradient boosting on decision trees library.
+- Time Series Forecasting
+  - [timesfm](https://github.com/google-research/timesfm) - A pretrained foundation model from Google Research for time-series forecasting.
 
 ### Natural Language Processing