Competition Series in Data Science and the Chance to Win Valuable Prizes

Kaggle invites all passionate about data science to a competition; 2021 Kaggle Machine Learning & Data Science Survey. Kaggle competitions are incredibly fun and rewarding, as well it is more beginner-friendly. The goal of these competitions is to provide a fun, and approachable for anyone, tabular dataset. These competitions will be great for people looking for something in between the Titanic Getting Started competition and a Featured competition.

If you are among the top three teams, you will get:

• Choice of Kaggle merchandise.

Please note: In order to encourage more participation from beginners, Kaggle merchandise will only be awarded once per person in this series.

You should follow the following terms:

• You must be: 
• a registered account holder at Kaggle.com.
• the older of 18 years old or the age of majority in your jurisdiction of residence.
•  not a resident of Crimea, Cuba, Iran, Syria, or North Korea.
•  not a person or representative of an entity under U.S. export controls or sanctions.
• You may submit a maximum of 5 entries per day.
• You may select up to 2 final submissions for judging.
• The maximum team size is 3.
• You cannot sign up to Kaggle from multiple accounts. 
• Privately sharing code or data outside of teams is not permitted.
• If you are entering as a representative of a company, educational institution or other legal entity, or on behalf of your employer, these rules are binding on you, individually, and the entity you represent.

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Timeline:

• Start Date: November 1, 2021.
• Team Merger deadline: November 30, 2021.
• Final submission deadline: November 30, 2021.

About Kaggle:

Kaggle, a subsidiary of Google LLC, is an online community of data scientists and machine learning practitioners. It got its start in 2010 by offering machine learning competitions and now also offers a public data platform, a cloud-based workbench for data science, and Artificial Intelligence education. Kaggle allows users to find and publish data sets, 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.

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