Comparative Analysis of Models Based on Titanic Survival Predictions

DOI

10.2991/978-94-6463-540-9_17How to use a DOI?

Keywords

Titanic; decision tree; random forest; survival prediction model; accident rescue

Abstract

Survival in natural disasters and major accidents is difficult to predict, and in many cases, it is difficult to extrapolate. In this paper, and the probability of survival of the Titanic tourists is discussed. First of all, the characteristics of the passengers in the Titanic data collection from the Kaggle, data processing and data analysis. In data processing, the method of taking the median is used to fill the vacancy of data, making the result become more accurate. Through Decision trees, Random Forest and the general survival prediction model experiments, the most suitable model was analyzed and its highest accuracy and accuracy was determined. It is found that the random forest model has high precision and accuracy in the application of survival prediction. The correlation survival prediction model has been applied to the survival prediction of natural disasters such as ship accidents at sea. Experiments show that the correlation model is reliable and effective, and the prediction model of survival rate is effective.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Yulin Huang
PY  - 2024
DA  - 2024/10/16
TI  - Comparative Analysis of Models Based on Titanic Survival Predictions
BT  - Proceedings of the 2024 2nd International Conference on Image, Algorithms and Artificial Intelligence (ICIAAI 2024)
PB  - Atlantis Press
SP  - 146
EP  - 153
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-540-9_17
DO  - 10.2991/978-94-6463-540-9_17
ID  - Huang2024
ER  -