Remove extra empty line, place TabGAN in alphabetical order

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

Author: Diyago

README.md

@@ -160,8 +160,6 @@ _Frameworks for Neural Networks and Deep Learning. Also see [awesome-deep-learni
 
 ## Machine Learning
 
-
-- [TabGAN](https://github.com/Diyago/Tabular-data-generation) - Synthetic tabular data generation using GANs, Diffusion Models, and LLMs.
 _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.
@@ -172,6 +170,7 @@ _Libraries for Machine Learning. Also see [awesome-machine-learning](https://git
 - [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.
 - [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.
 - [xgboost](https://github.com/dmlc/xgboost) - A scalable, portable, and distributed gradient boosting library.
 
 ## Natural Language Processing