Kaggle
Showing 16 of 16 projects
A high-performance gradient boosting library with best-in-class handling of categorical features and support for CPU/GPU training.
A curated collection of hands-on data science project ideas and resources for learning machine learning and AI concepts.
Docker image providing the Python environment used by Kaggle Notebooks for data science competitions.
MLBox is a powerful Automated Machine Learning python library.
An automated feature generation framework for tabular data that discovers expert-level features to boost machine learning model performance.
A hyperparameter-free gradient boosting machine with a simple budget parameter, built for high performance with Rust and bindings for Python and R.
A Python package for stacking (stacked generalization) with both functional and scikit-learn compatible APIs.
An open-source machine learning solution for the Home Credit Default Risk Kaggle competition, providing reproducible code and experiments.
A tree ensemble machine learning method that delivers better results than gradient boosted decision trees on many datasets.
An intelligent data search and enrichment library for machine learning that automatically finds and adds relevant external features to ML pipelines.
Course materials for GWU's Data Mining and Machine Learning classes covering preprocessing, modeling, and practical Kaggle applications.
Open-source implementation of the winning solution for the 2018 Data Science Bowl Kaggle competition using PyTorch and U-Net.
An open-source starter solution for the Kaggle Toxic Comment Classification Challenge, providing ready-to-use machine learning pipelines for detecting online harassment.
A collection of scripts for training random forests and sparse filtering models on Kaggle datasets.
A collection of scripts for processing Merck molecular activity challenge data from Kaggle into formats suitable for machine learning.
A small dataset for the Best Buy mobile contest on Kaggle, containing product information and search queries.
Found a gem we're missing?
Open-Awesome is built by the community, for the community. Submit a project, suggest an awesome list, or help improve the catalog on GitHub.