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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation
| Date | Stars |
|---|---|
| 2026-07-24 | 3278 |
| 2026-07-25 | 3278 |
| 2026-07-28 | 3278 |
| 2026-07-30 | 3278 |
| 2026-08-06 | 3278 |
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# MLJAR Automated Machine Learning for Humans
[](https://github.com/mljar/mljar-supervised/actions/workflows/run-tests.yml)
[](https://badge.fury.io/py/mljar-supervised)
[](https://anaconda.org/conda-forge/mljar-supervised)
[](https://anaconda.org/conda-forge/mljar-supervised)
[](https://pepy.tech/project/mljar-supervised)
<p align="center">
<img
alt="mljar AutoML"
src="https://raw.githubusercontent.com/mljar/mljar-examples/master/media/AutoML_white.png#gh-light-mode-only" width="50%" />
</p>
<p align="center">
<img
alt="mljar AutoML"
src="https://raw.githubusercontent.com/mljar/mljar-examples/master/media/AutoML_black.png#gh-dark-mode-only" width="50%" />
</p>
---
**Documentation**: <a href="https://supervised.mljar.com/" target="_blank">https://supervised.mljar.com/</a>
**Source Code**: <a href="https://github.com/mljar/mljar-supervised" target="_blank">https://github.com/mljar/mljar-supervised</a>
**Use MLJAR Studio (Easiest Way to Run MLJAR AutoML)**
MLJAR Studio is the easiest way to run MLJAR AutoML, with conversational notebooks and AutoLab experiments (autonomous AI for ML pipeline optimization).
Start quickly, iterate faster, and manage experiments in one place.
[Try MLJAR Studio](https://mljar.com)
<p align="center">
<img src="https://raw.githubusercontent.com/mljar/mljar-examples/master/media/pipeline_AutoML.png" width="100%" />
</p>
---
## Table of Contents
- [Automated Machine Learning](https://github.com/mljar/mljar-supervised#automated-machine-learning)
- [Generate Web Apps for Trained Models](https://github.com/mljar/mljar-supervised#generate-web-apps-for-trained-models)
- [What's good in it?](https://github.com/mljar/mljar-supervised#whats-good-in-it)
- [Automatic Documentation](https://github.com/mljar/mljar-supervised#automatic-documentation)
- [Available Modes](https://github.com/mljar/mljar-supervised#available-modes)
- [Fairness Aware Training](https://github.com/mljar/mljar-supervised#fairness-aware-training)
- [Examples](https://github.com/mljar/mljar-supervised#examples)
- [FAQ](https://github.com/mljar/mljar-supervised#faq)
- [Documentation](https://github.com/mljar/mljar-supervised#documentation)
- [Installation](https://github.com/mljar/mljar-supervised#installation)
- [Demo](https://github.com/mljar/mljar-supervised#demo)
- [Contributing](https://github.com/mljar/mljar-supervised#contributing)
- [Cite](https://github.com/mljar/mljar-supervised#cite)
- [License](https://github.com/mljar/mljar-supervised#license)
- [Commercial support](https://github.com/mljar/mljar-supervised#commercial-support)
- [MLJAR](https://github.com/mljar/mljar-supervised#mljar)
# Automated Machine Learning
The `mljar-supervised` is an Automated Machine Learning Python package that works with tabular data. It is designed to save time for a data scientist. It abstracts the common way to preprocess the data, construct the machine learning models, and perform hyper-parameters tuning to find the best model :trophy:. It is no black box, as you can see exactly how the ML pipeline is constructed (with a detailed Markdown report for each ML model).
The `mljar-supervised` will help you with:
- explaining and understanding your data through model reports, feature importance, and SHAP explanations,
- trying many different machine learning models (Algorithm Selection and Hyper-Parameters tuning),
- creating Markdown reports from analysis with details about all models (Automatic-Documentation),
- generating a web app for a trained model, so predictions can be used bExcerpt of 27,518 characters
Read on GitHub
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HRUSHIKESH DOKALA · @atlanhq · India
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Aaron Schumacher
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:96166aa58b519e35, topic:neural-network