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.
+155 stars
from 2,467 · score 5.05
Trending repositories
Ranked by Attention Score: stars gained over the last 180 complete days
11 repositories by Attention Score · May 7, 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.
+110 stars
from 2,259 · score 4.71
3.alteryx / featuretools
An open source python library for automated feature engineering
+92 stars
from 7,602 · score 4.53
4.microsoft / nniArchived
An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.
+85 stars
from 14,281 · score 4.45
5.feature-engine / feature_engine
Feature engineering and selection open-source Python library compatible with sklearn.
+75 stars
from 2,221 · score 4.33
6.EpistasisLab / tpot
A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.
+55 stars
from 10,000 · score 4.03
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.
+38 stars
from 477 · score 3.66
8.rodrigo-arenas / Sklearn-genetic-opt
Hyperparameter optimization and feature selection for scikit-learn using evolutionary algorithms. A modern alternative to GridSearchCV and RandomizedSearchCV.
+37 stars
from 354 · score 3.64
9.logicalclocks / hopsworks
Hopsworks - Data-Intensive AI platform with a Feature Store
+23 stars
from 1,288 · score 3.18
10.azukds / tubular
Python package implementing ML feature engineering and pre-processing for polars or pandas dataframes.
+11 stars
from 98 · score 2.48
11.Diyago / Tabular-data-generation
We well know GANs for success in the realistic image generation. However, they can be applied in tabular data generation. We will review and examine some recent papers about tabular GANs in action.
+5 stars
from 565 · score 1.79