Kaggle Competitions is a core feature of Kaggle, a data science competition platform and online community for data scientists and machine learning practitioners, owned by Google LLC. Since its founding in April 2010 by Anthony Goldbloom, Kaggle has enabled users to find and publish datasets, build models in a web-based environment, and enter competitions to solve data science challenges. The platform has grown to over 15 million users in 194 countries as of October 2023, and its competitions have become a benchmark for innovation in Machine learning and Artificial intelligence. However, the platform has also faced criticism for facilitating the use of unethical and unreliable data in medical research.
History
Kaggle was founded by Anthony Goldbloom in April 2010. Jeremy Howard, one of the first users, joined in November 2010 and served as President and Chief Scientist, with Nicholas Gruen as founding chair. In 2011, the company raised $12.5 million, and Max Levchin became chairman. On March 8, 2017, Fei-Fei Li, then Chief Scientist at Google, announced Google's acquisition of Kaggle. In June 2017, Kaggle surpassed 1 million registered users, and by October 2023 it had over 15 million users across 194 countries. In 2022, founders Goldbloom and Ben Hamner stepped down, and D. Sculley became CEO. In February 2023, Kaggle introduced Models, allowing users to discover and use pre-trained models with deep integrations. In April 2025, Kaggle partnered with the Wikimedia Foundation.
Competitions
Many machine-learning competitions have been run on Kaggle since its founding. Notable examples include gesture recognition for Microsoft Kinect, an association football AI for Manchester City, a trading algorithm for Two Sigma Investments, and improving the search for the Higgs boson at CERN. Competition hosts prepare data and problem descriptions, optionally offering prize money. Participants experiment with techniques and compete to produce the best models, sharing work publicly through Kaggle Notebooks (formerly Kernels). Submissions are made via Notebooks, manual upload, or the Kaggle API. Most competitions score submissions immediately against a hidden solution and display results on a live leaderboard. Winners grant the host a worldwide, perpetual, irrevocable, and royalty-free license to use their winning entry, which is non-exclusive unless otherwise specified.
Kaggle also offers private competitions limited to top participants, a free tool for academic competitions, and recruiting competitions where data scientists compete for interviews at companies like Facebook, Winton Capital, and Walmart. Successful projects have advanced HIV research, chess ratings, and traffic forecasting. Notably, Geoffrey Hinton and George Dahl used Deep learning with Neural networks to win a Merck competition, and Vlad Mnih, a Hinton student, won an Adzuna competition using similar techniques, popularizing deep learning in the community. Tianqi Chen from the University of Washington demonstrated the power of XGBoost, which has since replaced Random Forest as a dominant method in Kaggle competitions. Several academic papers have been published based on competition findings, often inspired by the live leaderboard's push for innovation.
Progression System
Kaggle has a progression system with five tiers: Novice, Contributor, Expert, Master, and Grandmaster. Tiers are earned by meeting criteria in competitions, datasets, notebooks, and discussions. The highest tier, Kaggle Grandmaster, requires top rankings in multiple competitions, including high ranking in a solo team. As of April 2, 2025, out of 23.29 million accounts, 2,973 achieved Master status and 612 achieved Grandmaster status.
Kaggle Notebooks
Kaggle Notebooks is a free, browser-based integrated development environment for data science and machine learning. Users can write and execute code in Python or R, import datasets, use popular libraries, and train models on CPUs, GPUs, or TPUs in the cloud. It is widely used for competition submissions, tutorials, education, and exploratory data analysis.
Medical Research Problems
Kaggle has faced scrutiny over datasets used in medical research. In December 2025, The Transmitter reported that Springer Nature retracted or removed nearly 40 publications that trained neural networks on a dataset of photographs of autistic and non-autistic children's faces, uploaded to Kaggle with over 2,900 images. Consent for research use was unlikely, and at least 90 other publications cited the dataset. In April 2026, Nature published findings about two datasets on Kaggle with no data provenance, used in 125 clinical prediction models, including two used in hospitals in Indonesia and Spain, and one referenced in a medical device patent. By June 5, 2026, five articles using these datasets were retracted. In May 2026, an article in Retraction Watch highlighted 'comically bad' datasets for stroke and diabetes, including images of celebrities like Sylvester Stallone and George Clooney, and children, with some images mislabeled. One dataset was removed from Kaggle, while the other remained with no consent for child images.