Kaggle

History

Kaggle was founded by Anthony Goldbloom in April 2010. Jeremy Howard, one of the first Kaggle users, joined in November 2010 and served as the President and Chief Scientist. Also on the team was Nicholas Gruen serving as the founding chair. In 2011, the company raised $12.5 million and Max Levchin became the chairman. On March 8, 2017, Fei-Fei Li, Chief Scientist at Google, announced that Google was acquiring Kaggle.

In June 2017, Kaggle surpassed 1 million registered users, and as of October 2023, it has over 15 million users in 194 countries.

In 2022, founders Goldbloom and Hamner stepped down from their positions and D. Sculley became the CEO.

In February 2023, Kaggle introduced Models, allowing users to discover and use pre-trained models through deep integrations with the rest of Kaggle’s platform.

In April 2025, Kaggle partnered with Wikimedia Foundation.

Site overview

Competitions

Many machine-learning competitions have been run on Kaggle since the company was founded. Notable competitions include gesture recognition for Microsoft Kinect, making a football AI for Manchester City, coding a trading algorithm for Two Sigma Investments, and improving the search for the Higgs boson at CERN.

The competition host prepares the data and a description of the problem; the host may choose whether it's going to be rewarded with money or be unpaid. Participants experiment with different techniques and compete against each other to produce the best models. Work is shared publicly through Kaggle Kernels to achieve a better benchmark and to inspire new ideas. Submissions can be made through Kaggle Kernels, via manual upload or using the Kaggle API. For most competitions, submissions are scored immediately (based on their predictive accuracy relative to a hidden solution file) and summarized on a live leaderboard. After the deadline passes, the competition host pays the prize money in exchange for "a worldwide, perpetual, irrevocable and royalty-free license [...] to use the winning Entry", i.e. the algorithm, software and related intellectual property developed, which is "non-exclusive unless otherwise specified".

Alongside its public competitions, Kaggle also offers private competitions, which are limited to Kaggle's top participants. Kaggle offers a free tool for data science teachers to run academic machine-learning competitions. Kaggle also hosts recruiting competitions in which data scientists compete for a chance to interview at leading data science companies like Facebook, Winton Capital, and Walmart.

Kaggle's competitions have resulted in successful projects such as furthering HIV research, chess ratings and traffic forecasting. Geoffrey Hinton and George Dahl used deep neural networks to win a competition hosted by Merck. Vlad Mnih (one of Hinton's students) used deep neural networks to win a competition hosted by Adzuna. This resulted in the technique being taken up by others in the Kaggle community. Tianqi Chen from the University of Washington also used Kaggle to show the power of XGBoost, which has since replaced Random Forest as one of the main methods used to win Kaggle competitions.

Several academic papers have been published based on findings from Kaggle competitions. A contributor to this is the live leaderboard, which encourages participants to continue innovating beyond existing best practices. The winning methods are frequently written on the Kaggle Winner's Blog.

Progression system

Kaggle has implemented a progression system to recognize and reward users based on their contributions and achievements within the platform. This system consists of five tiers: Novice, Contributor, Expert, Master, and Grandmaster. Each tier is achieved by meeting specific criteria in competitions, datasets, kernels (code-sharing), and discussions.

The highest tier, Kaggle Grandmaster, is awarded to users who have ranked at the top of multiple competitions including high ranking in a solo team. As of April 2, 2025, out of 23.29 million Kaggle accounts, 2,973 have achieved Kaggle Master status and 612 have achieved Kaggle Grandmaster status.

Kaggle Notebooks

Kaggle includes a free, browser-based online integrated development environment, called Kaggle Notebooks, designed 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 directly in the cloud. This environment is often used for competition submissions, tutorials, education, and exploratory data analysis.

References

References

1. (2023-04-17). "A Beginner's Guide to Kaggle for Data Science".
2. (March 8, 2017). "Google is acquiring data science community Kaggle". [[Techcrunch]].
3. "The exabyte revolution: how Kaggle is turning data scientists into rock stars".
4. Mulcaster, Glenn. (4 November 2011). "Local minnow the toast of Silicon Valley". The Sydney Morning Herald.
5. Lichaa, Zachary. "Max Levchin Becomes Chairman Of Kaggle, A Startup That Helps NASA Solve Impossible Problems". Business Insider.
6. "Welcome Kaggle to Google Cloud". Google Cloud Platform Blog.
7. "Unique Kaggle Users".
8. Markoff, John. (24 November 2012). "Scientists See Advances in Deep Learning, a Part of Artificial Intelligence". The New York Times.
9. (2017-06-06). "We've passed 1 million members". Kaggle Winner's Blog.
10. Wali, Kartik. (2022-06-08). "Kaggle gets new CEO, founders quit after a decade".
11. "[Product Launch] Introducing Kaggle Models | Data Science and Machine Learning".
12. (2025-04-16). "Kaggle and the Wikimedia Foundation are partnering on open data.".
13. Byrne, Ciara. (December 12, 2011). "Kaggle launches competition to help Microsoft Kinect learn new gestures". VentureBeat.
14. Wigglesworth, Robin. (March 8, 2017). "Hedge funds adopt novel methods to hunt down new tech talent". [[The Financial Times]].
15. (July 15, 2014). "The machine learning community takes on the Higgs". Symmetry Magazine.
16. Kaggle. "Terms and Conditions - Kaggle".
17. Kaggle. "Kaggle in Class".
18. Carpenter, Jennifer. (February 2011). "May the Best Analyst Win". Science Magazine.
19. Sonas, Jeff. (20 February 2011). "The Deloitte/FIDE Chess Rating Challenge". Chessbase.
20. Foo, Fran. (April 6, 2011). "Smartphones to predict NSW travel times?". The Australian.
21. "NIPS 2014 Workshop on High-energy Physics and Machine Learning".
22. (2011). "The Value of Feedback in Forecasting Competitions". International Journal of Forecasting.
23. "CSE 40657/60657: Natural Language Processing".
24. (25 February 2022). "Underrated Kaggle notebooks every data science enthusiast must know | AIM".