Awesome Python Data Science

moDel Agnostic Language for Exploration and eXplanation

A Graph Neural Network Library in Jax

Metric learning algorithms in Python

A research toolkit for particle swarm optimization in Python

Transpile trained scikit-learn estimators to C, Java, JavaScript and others.

Scalable machine 🤖 learning for time series forecasting.

:chart_with_upwards_trend: Adaptive: parallel active learning of mathematical functions

PySpark + Scikit-learn = Sparkit-learn

An autoML framework & toolkit for machine learning on graphs.

fastFM: A Library for Factorization Machines

Modular Reinforcement Learning (RL) library (implemented in PyTorch, JAX, and NVIDIA Warp) with support for Gymnasium/Gym, NVIDIA Isaac Lab, MuJoCo Playground and other environments

allRank is a framework for training learning-to-rank neural models based on PyTorch.

[CONTRIBUTORS WELCOME] Generalized Additive Models in Python

Pretrained model hub for Keras 3.

A scikit-learn based module for multi-label et. al. classification

A PyTorch and TorchDrug based deep learning library for drug pair scoring. (KDD 2022)

ML powered analytics engine for outlier detection and root cause analysis.

Little Ball of Fur - A graph sampling extension library for NetworKit and NetworkX (CIKM 2020)

ThunderGBM: Fast GBDTs and Random Forests on GPUs

Framework and Library for Distributed Online Machine Learning

Python package for stacking (machine learning technique)

A collection of state-of-the-art algorithms for the training, serving and interpretation of Decision Forest models in Keras.

🍇 GRAPE is a Rust/Python Graph Representation Learning library for Predictions and Evaluations

🎯 A comprehensive gradient-free optimization framework written in Python

QKeras: a quantization deep learning library for Tensorflow Keras

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.

Module for statistical learning, with a particular emphasis on time-dependent modelling

Cleora AI is a general-purpose open-source model for efficient, scalable learning of stable and inductive entity embeddings for heterogeneous relational data. Created by Synerise.com team.

Python package for Bayesian Machine Learning with scikit-learn API

🎆 A visualization of the CapsNet layers to better understand how it works

Hyperparameter optimization and feature selection for scikit-learn using evolutionary algorithms. A modern alternative to GridSearchCV and RandomizedSearchCV.

A unified framework for tabular probabilistic regression, time-to-event prediction, and probability distributions in python

Bayesian Optimization using GPflow

zoofs is a python library for performing feature selection using a variety of nature-inspired wrapper algorithms. The algorithms range from swarm-intelligence to physics-based to Evolutionary. It's easy to use , flexible and powerful tool to reduce your feature size.

Python-based implementations of algorithms for learning on imbalanced data.

A library for augmenting annotated audio data

A fast xgboost feature selection algorithm

The official implementation of "The Shapley Value of Classifiers in Ensemble Games" (CIKM 2021).

BatchFlow helps you conveniently work with random or sequential batches of your data and define data processing and machine learning workflows even for datasets that do not fit into memory.

⬛ Python Individual Conditional Expectation Plot Toolbox

PyTorch Geometric Signed Directed is a signed/directed graph neural network extension library for PyTorch Geometric. The paper is accepted by LoG 2023.

Library for machine learning stacking generalization.

NitroFE is a Python feature engineering engine which provides a variety of modules designed to internally save past dependent values for providing continuous calculation.

Python package implementing ML feature engineering and pre-processing for polars or pandas dataframes.

Support vector machines (SVMs) and related kernel-based learning algorithms are a well-known class of machine learning algorithms, for non-parametric classification and regression. liquidSVM is an implementation of SVMs whose key features are: fully integrated hyper-parameter selection, extreme speed on both small and large data sets, full flexibility for experts, and inclusion of a variety of different learning scenarios: multi-class classification, ROC, and Neyman-Pearson learning, and least-squares, quantile, and expectile regression.

Contrastive Explanation (Foil Trees), developed at TNO/Utrecht University

A collection of pandas & scikit-learn compatible transformers for preprocessing and feature engineering 🛠

TensorLight - A high-level framework for TensorFlow