Abstract
The Royal Mail Ship (RMS) Titanic is the largest liner built in 1912, of the estimated 2224 passengers and crew aboard, more than 1500 died after the ship struck an iceberg during her maiden voyage from Southampton to New York City. The dataset collected from Kaggle has information about the passengers and crew which are P-class, name, age, sex, etc., and can be used to predict if the person onboard has survived or not. In this paper, six different machine learning algorithms are used (i.e., logistic regression, k-nearest neighbors, SVM, naive Bayes, decision tree and random forest) to study this dataset and deduce useful information to know the knowledge of the reasons for the survival of some travelers and sinking the rest. Finally, the results have been analyzed and compared with and without cross-validation to evaluate the performance of each classifier.
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Tabbakh, A., Rout, J.K., Rout, M. (2021). Analysis and Prediction of the Survival of Titanic Passengers Using Machine Learning. In: Tripathy, A., Sarkar, M., Sahoo, J., Li, KC., Chinara, S. (eds) Advances in Distributed Computing and Machine Learning. Lecture Notes in Networks and Systems, vol 127. Springer, Singapore. https://doi.org/10.1007/978-981-15-4218-3_29
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DOI: https://doi.org/10.1007/978-981-15-4218-3_29
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