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.
+20 stars
from 2,602 · score 3.04
Trending repositories
Ranked by Attention Score: stars gained over the last 30 complete days
10 repositories by Attention Score · Oct 4, 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.
+20 stars
from 2,349 · score 3.04
3.feature-engine / feature_engine
Feature engineering and selection open-source Python library compatible with sklearn.
+11 stars
from 2,285 · score 2.48
4.alteryx / featuretools
An open source python library for automated feature engineering
+10 stars
from 7,684 · score 2.40
5.EpistasisLab / tpot
A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.
+7 stars
from 10,048 · score 2.08
6.microsoft / nniArchived
An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.
+5 stars
from 14,361 · score 1.79
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.
+5 stars
from 510 · score 1.79
8.logicalclocks / hopsworks
Hopsworks - Data-Intensive AI platform with a Feature Store
+3 stars
from 1,308 · score 1.39
9.rodrigo-arenas / Sklearn-genetic-opt
Hyperparameter optimization and feature selection for scikit-learn using evolutionary algorithms. A modern alternative to GridSearchCV and RandomizedSearchCV.
+1 stars
from 390 · score 0.69
10.azukds / tubular
Python package implementing ML feature engineering and pre-processing for polars or pandas dataframes.
+1 stars
from 108 · score 0.69