Kaggle Python Packages

lightgbm

A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.

catboost

A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.

xgboost-ray

Distributed XGBoost on Ray

jupyter-kernel-client

🪐 > Jupyter Kernel Client through HTTP and WebSocket.

catboost-dev

A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.

upgini

Data search & enrichment library for Machine Learning → Easily find and add relevant features to your ML & AI pipeline from hundreds of public and premium external data sources, including open & commercial LLMs

rgf-python

Home repository for the Regularized Greedy Forest (RGF) library. It includes original implementation from the paper and multithreaded one written in C++, along with various language-specific wrappers.

pytorch-toolbelt

PyTorch extensions for fast R&D prototyping and Kaggle farming

lightautoml

Fast and customizable framework for automatic ML model creation (AutoML)

kts

Interactive ML Toolset

goto-conversion

goto_conversion - Powered $47,000 of prize money, 10+ Gold Medals and 100+ Medals on Kaggle

lucifer-ml

Semi-Auto Machine Learning Library by d4rk-lucif3r

hypex

Fast and customizable framework for automatic and quick Causal Inference in Python

kaggler

Code for Kaggle Data Science Competitions

kagglex

Execute local Python code, modules, and experiments seamlessly on Kaggle GPUs and TPUs.

oev

An 184M-parameter decision engine delivering calibrated distributions from one 22ms forward pass while matching models 2.3x its size.

chefboost

A Lightweight Decision Tree Framework supporting regular algorithms: ID3, C4.5, CART, CHAID and Regression Trees; some advanced techniques: Gradient Boosting, Random Forest and Adaboost w/categorical features support for Python

dao-scripts

Scripts to get data for the DAO-Analyzer dataset

kctx

Turn any Kaggle competition into Claude-ready context — workspace folder, Claude skill, or local MCP server. One curl, one TUI.

kdev-cli

A Kaggle notebook as your remote dev box: SSH and VS Code in, files that persist across sessions, accounts and machines.

superii-sdk

Super ii Open intelligence, built together. A public collaboration hub for Ai models.

kaggle-vllm

Run upstream vLLM on Kaggle dual NVIDIA Tesla T4 GPUs with a validated CUDA runtime, safe staging, NCCL tensor parallelism, and reproducible compatibility checks.

ariel-data-preprocessing

Kaggle competition: NeurIPS - Ariel Data Challenge 2025

fuggle

Fugue for Kaggle users