Kaggle

Kaggle
	Kaggle logotype
Company type	Subsidiary
Industry	Data science
Founded	April 2010
Founder	Anthony Goldbloom; Ben Hamner;
Headquarters	San Francisco, United States
Key people	D. Sculley (CEO); Julia Elliott (COO) ; Jeff Moser (Chief Architect) ;
Products	Competitions, Kaggle Kernels, Kaggle Datasets, Kaggle Learn
Parent	Google; (2017–present)
Website	kaggle.com

Kaggle is a data science competition platform and online community of data scientists and machine learning practitioners under Google LLC. Kaggle enables users to find and publish datasets, explore and build models in a web-based data science environment, work with other data scientists and machine learning engineers, and enter competitions to solve data science challenges.^[1]

History

Kaggle was founded by Anthony Goldbloom and Ben Hamner in April 2010.^[2] Jeremy Howard, one of the first Kaggle users, joined in November 2010 and served as the President and Chief Scientist.^[3] Also on the team was Nicholas Gruen serving as the founding chair.^[4] In 2011, the company raised $12.5 million and Max Levchin became the chairman.^[5] On 8 March 2017, Fei-Fei Li, Chief Scientist at Google, announced that Google was acquiring Kaggle.^[6]

In June 2017, Kaggle surpassed 1 million registered users, and as of October 2023, it has over 15 million users in 194 countries.^[7]^[8]^[9]

In 2022, founders Goldbloom and Hamner stepped down from their positions and D. Sculley became the CEO.^[10]

In February 2023, Kaggle introduced Models which allowed users to discover and use pre-trained models through deep integrations with the rest of Kaggle’s platform.^[11]

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,^[12] making a football AI for Manchester City, coding a trading algorithm for Two Sigma Investments,^[13] and improving the search for the Higgs boson at CERN.^[14]

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".^[15]

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.^[16] 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,^[17] chess ratings^[18] and traffic forecasting.^[19] Geoffrey Hinton and George Dahl used deep neural networks to win a competition hosted by Merck.^{[citation needed]} Vlad Mnih (one of Hinton's students) used deep neural networks to win a competition hosted by Adzuna.^{[citation needed]} 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.^{[citation needed]}

Several academic papers have been published on the basis of findings made in Kaggle competitions.^[20] A contributor to this is the live leaderboard, which encourages participants to continue innovating beyond existing best practices.^[21] 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.^[22]

The highest and most prestigious tier, Kaggle Grandmaster, is awarded to users who demonstrate exceptional skills in data science and machine learning. Achieving this status is extremely challenging. As of May 28, 2024, out of 18.5 million Kaggle users, only 2,745 (about 1 out of every 6750 users) have reached the Master level.

Among these Masters, only 530 (approximately 1 out of every 5 Masters) have achieved the coveted Kaggle Grandmaster status.^[23]

The other tiers in the progression system include: