Towards accurate titanic disaster competition via machine learning algorithms

Yufan Wei

doi:10.1117/12.2672702

28 March 2023 Towards accurate titanic disaster competition via machine learning algorithms

Yufan Wei

Proceedings Volume 12597, Second International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2022); 125973O (2023) https://doi.org/10.1117/12.2672702
Event: Second International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2022), 2022, Nanjing, China

Abstract

The Titanic shipwreck, of the estimated 2,224 passengers and crew aboard, more than 1,500 died, making it the deadliest sinking of a single ship up to that time. It remains the fatal peacetime sinking of a super liner ship. While some elements of luck are involved in surviving, research on understanding what impacts people’s survival or death is crucial. In this study, the utilized dataset includes various features, and we attempt to determine the correlation among these features. Also, various machine learning algorithms are proposed to predict the survival of passengers.

Citation Download Citation

Yufan Wei "Towards accurate titanic disaster competition via machine learning algorithms", Proc. SPIE 12597, Second International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2022), 125973O (28 March 2023); https://doi.org/10.1117/12.2672702

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available