Research on Titanic Survival Prediction Based on Machine Learning Method Article Swipe

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Yongzhong Liao , Shimiao Zhang , Zixin Zhang ·

YOU? · · 2025 · Open Access · · DOI: https://doi.org/10.54254/2754-1169/2024.19451

On April 15, 1912, the British luxury passenger ship Titanic sank on its maiden voyage from Southampton to New York because of a collision with an iceberg, resulting in the death of 1502 out of 2224 passengers and crew. This article gains insight into the factors that influence the survival rate of passengers on the Titanic and establish a model of hard voting consisting of logistic regression, random forest and decision tree to predict what sort of people are more likely to survive in this catastrophe. The process involves dealing with the missing values, creating new variables by feature engineering and fitting the model to the dataset. The overall model performs well accuracy 87.64%. By applying to the navigation field, more data can be collected and more precise predictions can be made. The results can also help individuals to predict risk factors and try to decrease them as much as possible while robustness and stability of the model still need to be refined.

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Artificial Intelligence

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sort Crew Decision tree Robustness (evolution) Random forest Computer science Logistic regression Feature (linguistics) Artificial intelligence Machine learning Operations research Engineering Aeronautics Information retrieval Chemistry Gene Biochemistry Philosophy Linguistics

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Type: article
Language: en
Landing Page: https://doi.org/10.54254/2754-1169/2024.19451
PDF: https://www.ewadirect.com/proceedings/aemps/article/view/19451/pdf
OA Status: hybrid
References: 7
Related Works: 10
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DOI: https://doi.org/10.54254/2754-1169/2024.19451

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Title: Research on Titanic Survival Prediction Based on Machine Learning Method

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Language: en

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Publication year: 2025

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Publication date: 2025-01-06

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Authors: Yongzhong Liao, Shimiao Zhang, Zixin Zhang

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Landing page: https://doi.org/10.54254/2754-1169/2024.19451

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Concepts: sort, Crew, Decision tree, Robustness (evolution), Random forest, Computer science, Logistic regression, Feature (linguistics), Artificial intelligence, Machine learning, Operations research, Engineering, Aeronautics, Information retrieval, Chemistry, Gene, Biochemistry, Philosophy, Linguistics

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