Sociological transactions play an important role in human behaviour and social standing. The Titanic was the perfect example as the passengers belonged to high income, middleincome, and low-income groups. It is interesting to see how social factors influenced who was going to survive. The data was collected from the website “Kaggle.com”, and machine learning algorithms were applied after carrying out an exploratory and visual analysis. The hypothesis that women and children were saved (which became famous after Steven Spielberg’s Titanic (1975)) was tested by random forest algorithm as well as the hypothesis that family density played a major role in survival. The results showed that title and sex were the most important factors influencing if the passenger was to survive.

---

Keywords:
Titanic,
social class,
survived,
sex,
family size

JEL Classification:
C02, C12, D91, Q59

RSN_9(20)_02_Surviving_Titanic

Beesley, L. (1912). The loss of the S.S.Titanic. New York: Dover Publications.

Breiman. (2001). Random forests. Machine Learning, 45(1), 5-32.

Bryceson, D. (1912). The Titanic disaster: British National Press. New York: W.W.      Norton & Company Inc.

Chen, Y., Sze, V. & Zhang, Z. (2017). Hardware for machine learning: Challenges and opportunities. 2017 IEEE Custom Integrated Circuits Conference, 1-8.

Cortes, C. & Vapnik, V. (1995). Support vector networks. Machine Learning, 20, 273-297.

Dacy, D. C. & Kunreuther, H. C. (1969). The economics of natural disasters. New York: Free Press.

Elinder, M., & Erixson, O. (2012). Gender, social norms, and survival in maritime disasters. Proceedings of the National Academy of Sciences, 109(33), 13220-13224.

Elster, J. (2007). Explaining social behavior. More nuts and bolts for the social sciences. Cambridge: Cambridge University Press.

Enarson, E. (2000). Gender and natural disasters (Working Paper 1). Recovery and Reconstruction Department, Geneva, September.

Erel, I., Stern, C. T. & Weisbach, M. (2017). Selecting directors using machine learning (Working Paper). University of Washington.

Frey, B. S., Savage, D. A., & Torgler, B. (2010). Noblesse oblige? Determinants of survival in a life and death situation. J Econ Behav Organ, 74, 1-11.

Frey, B. S., Savage, D. A. & Torgler, B. (2011). Behavior under extreme conditions: The Titanic disaster. J Econ Perspect, 25, 209-222.

Frey, B., Savage, D. & Torgler, B. (2001). Behavior under extreme conditions: The Titanic disaster. Journal of Economic Perspectives, 25(1), 209-222. Doi: 10.1257/ jep.25.1.209.

Geller, J. B. (1998). Titanic: Women and children first. New York: W.W. Norton & Company Inc.

Gleicher, D. & Stevans, L. K. (2004). Who survived the Titanic? A logistic regression analysis. Int Rev Marit Hist, 16, 61-94.

Hall, W. (1986). Social class and survival on the S.S. Titanic. Soc Sci Med 22, 687-690.

Labib, A. & Read, M. (2013). Not just rearranging the deckchairs on the Titanic: Learning from failures through risk and reliability analysis. Safety Science, 51, 397-413.

Malioutov, D. M., Varshney, K. R., Emad, A. & Dash, S. (2017). Learning interpretable classification rules with Boolean compressed sensing. In A. A. Editor & B. B. Editor (Eds.), Transparent data mining for big and small data (pp. 95-121). Springer International Publishing.

Mawson, A. R. (2007). Mass panic and social attachment: The dynamics of human behavior. Ashgate Publishing, Aldershot, UK.

Mersey, L. (1912). Loss of the steamship Titanic. British Sessional Papers House of Commons, 1911-1912.

Neller, T. W. (2017). AI education: Machine-learning resources. AI Matters, 3(2), 14-15.

Russell, J. S. & Norvig, P. (2003). Artificial intelligence: A modern approach, 1(1) Pearson Education, 697-702.

Silver, D., Huang ,A., Maddison, C., Guez, A., Sifre, G., Driessche, G., Schrittwieser., Antonoglou, I., Panneershelvam, V., Lancot, M., Dieleman, S., Grewe, D., Nham, J., Kalchbrenner, N., Sutskever, I., Lillicrap, T., Leach, M., Kavukcuoglu, K., Graepal, T. & Hassabis, D. (2016). Mastering the game of go with deep neural networks and tree search. Nature, 529, 484-489.

Titanic: Machine Learning from Disaster. (2013). Retrieved from: https://www.kaggle.com.

Tuckett, D. (1976). An introduction to medical sociology. Tavistock Publications, London. Journal of Social Policy, 6(2), 227-229.

Turner, B. A. & Pidgeon, N. F. (1997). Man-made disasters (2nd ed). London: Butterworth Heinemann.

Whitley, A. (2015). Using statistical learning to predict survival of passengers on the RMS Titanic. Kansas: Kansas State University Manhattan.

Dimensions

PlumX