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Add Disco — automated pattern discovery for tabular data #88

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1 change: 1 addition & 0 deletions README.md
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* [PyCaret](https://github.com/pycaret/pycaret) - An open-source, low-code machine learning library in Python. <img height="20" src="img/R_big.png" alt="R inspired lib">
* [Shogun](https://github.com/shogun-toolbox/shogun) - Machine learning toolbox.
* [xLearn](https://github.com/aksnzhy/xlearn) - High Performance, Easy-to-use, and Scalable Machine Learning Package.
* [Disco](https://github.com/leap-laboratories/discovery-engine) - Superhuman exploratory data analysis. Finds the feature interactions and subgroup effects in tabular data that LLMs and manual exploration miss — with p-values, effect sizes, and literature citations. Free for public data.
* [cuML](https://github.com/rapidsai/cuml) - RAPIDS Machine Learning Library. <img height="20" src="img/sklearn_big.png" alt="sklearn"> <img height="20" src="img/gpu_big.png" alt="GPU accelerated">
* [modAL](https://github.com/cosmic-cortex/modAL) - Modular active learning framework for Python3. <img height="20" src="img/sklearn_big.png" alt="sklearn">
* [Sparkit-learn](https://github.com/lensacom/sparkit-learn) - PySpark + scikit-learn = Sparkit-learn. <img height="20" src="img/sklearn_big.png" alt="sklearn"> <img height="20" src="img/spark_big.png" alt="Apache Spark based">
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