Competition in the Field of Machine Learning from Kaggle and a Unique Chance to be Professional
Applicant criteria
- 18 - 60
- Both
Opportunity criteria
Opportunity description
Kaggle invites everyone with a passion for data science to a competition; Titanic - Machine Learning from Disaster, that requires to use of machine learning to create a model that predicts which passengers survived the Titanic shipwreck. This is the legendary Titanic ML competition, one of Kaggle's competitions that are incredibly fun and the best for you to dive into ML competitions.
Benefits
You will have a unique chance to:
- get familiar with Kaggle’s platform.
- learn basic machine learning concepts.
- start meeting people in the community.
Please note: There is no cash prize.
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Eligibility criteria
To be eligible to enter the Competition, you must be:
- A registered account holder at Kaggle.com.
- Older of 18 years old or the age of majority in your jurisdiction of residence.
- You can participate individually or in a team and the maximum team size is 10.
- Not a resident of Crimea, Cuba, Iran, Syria or North Korea.
- You may submit a maximum of 10 entries per day.
- You may select up to 5 final submissions for judging.
- You should submit a csv file with exactly 418 entries.
- The file should have exactly 2 columns:
- PassengerId (sorted in any order).
- Survived (contains your binary predictions: 1 for survived, 0 for deceased).
- Your work must not infringe any proprietary rights, intellectual property rights, or other rights of a third party.
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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