1.apache / hamilton
Apache Hamilton helps data scientists and engineers define testable, modular, self-documenting dataflows, that encode lineage/tracing and metadata. Runs and scales everywhere python does.
+67 stars
from 2,555 · score 4.22
Trending repositories
Ranked by Attention Score: stars gained over the last 90 complete days
10 repositories by Attention Score · Aug 5, 2026 – Nov 2, 2026 (UTC)
2.rorysroes / SGX-Full-OrderBook-Tick-Data-Trading-Strategy
Providing the solutions for high-frequency trading (HFT) strategies using data science approaches (Machine Learning) on Full Orderbook Tick Data.
+53 stars
from 2,316 · score 3.99
3.alteryx / featuretools
An open source python library for automated feature engineering
+43 stars
from 7,651 · score 3.78
4.feature-engine / feature_engine
Feature engineering and selection open-source Python library compatible with sklearn.
+32 stars
from 2,264 · score 3.50
5.microsoft / nniArchived
An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.
+27 stars
from 14,339 · score 3.33
6.EpistasisLab / tpot
A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.
+27 stars
from 10,028 · score 3.33
7.Desbordante / desbordante-core
Desbordante is a high-performance data profiler that is capable of discovering many different patterns in data using various algorithms. It also allows to run data cleaning scenarios using these algorithms. Desbordante has a console version and an easy-to-use web application.
+22 stars
from 493 · score 3.14
8.logicalclocks / hopsworks
Hopsworks - Data-Intensive AI platform with a Feature Store
+12 stars
from 1,299 · score 2.56
9.rodrigo-arenas / Sklearn-genetic-opt
Hyperparameter optimization and feature selection for scikit-learn using evolutionary algorithms. A modern alternative to GridSearchCV and RandomizedSearchCV.
+5 stars
from 386 · score 1.79
10.azukds / tubular
Python package implementing ML feature engineering and pre-processing for polars or pandas dataframes.
+2 stars
from 107 · score 1.10