Kaggle
Showing 13 of 13 projects
A high-performance gradient boosting library with best-in-class handling of categorical features and support for CPU/GPU training.
Docker image providing the Python environment used by Kaggle Notebooks for data science competitions.
A curated collection of hands-on data science project ideas and resources for learning machine learning and AI concepts.
An automated feature generation framework for tabular data that discovers expert-level features to boost machine learning model performance.
A Python package for stacking (stacked generalization) with both functional and scikit-learn compatible APIs.
A hyperparameter-free gradient boosting machine with a simple budget parameter, built for high performance with Rust and bindings for Python and R.
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.
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