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Kaggle is a data science competition platform and online community for 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.
<noinclude>[[Category:Competition record infobox templates]]</noinclude> to the end of the template code, making sure it starts on the same line as the code's last character. See also: Category:Medal infobox templates
The Netflix Prize is one such competition. Since then there have been several platforms developed on the idea of data science competitions. Research has been completed on how competition can improve research performance. Companies like JPMorgan Chase also run internal contests involving large numbers of employees. [2]
The purpose of this template is to provide an infobox summary for articles on specific events of a championship or games, for example Athletics at the 2012 Summer Olympics – Men's 100 metres. This template is designed for usage on any grouping of articles which describe individual competitions within a broader sports event.
Smart Agriculture Competition is an annual greenhouse challenge and agricultural productivity competition launched by the largest agriculture technology platform Pinduoduo to encourage the use of data-driven tools to improve agricultural productivity and environmental sustainability. [1] [2] [3]
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Create a data row for a football team's cup results table Template parameters [Edit template data] Parameter Description Type Status date date Date of the match Date required date-format date-format Date format, e.g. dmy or mdy Default dmy String required date-note date-note Used to add a footnote for the date String suggested round round Round of the competition, e.g. First round or Quarter ...
Data-driven models encompass a wide range of techniques and methodologies that aim to intelligently process and analyse large datasets. Examples include fuzzy logic, fuzzy and rough sets for handling uncertainty, [3] neural networks for approximating functions, [4] global optimization and evolutionary computing, [5] statistical learning theory, [6] and Bayesian methods. [7]