Learning From Disasters

Learning From Disasters
Author(s):
B.Rishi Srivathsava, P.Vikas, B.Sai Teja Goud, Mrs K. Sreelatha, P. Vikas
Keywords:
Learning From Disasters
Abstract
By using traditional machine learning algorithms, this study seeks to understand the relationship between the survival and mortality rates of passengers on the Titanic RMS. The passenger class, age, sex, passenger ID, and the number of siblings were all included in the dataset that was utilised for analysis, which was downloaded from the Kaggle website. Decision trees, random forests, xg-boost, the gradient boosting method, K-Nearest Neighbours, and logistic regression are just a few of the algorithms that are compared in terms of prediction accuracy in this study. Utilising the distinctive insights and extremely accurate numbers produced by each algorithm, the examination concentrates on the accuracy and precision of each approach. With the help of this study, we hope to advance our understanding of disaster analysis and offer new perspectives on how to make maritime accidents safer in the future
Article Details
Unique Paper ID: 160213 Publication Volume & Issue: Volume 10, Issue 1 Page(s): 146 - 149
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NCSEM 2024

National Conference on Sustainable Engineering and Management - 2024

Last Date: 15th March 2024

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