Surviving the Titanic Tragedy: A Sociological Study Using Machine Learning Models (Sobreviviendo a la tragedia del Titanic: un estudio sociológico utilizando modelos de aprendizaje automático)

Revista Suma de Negocios 9 (20) 86-92 2018

7 Pages Posted: 11 Jul 2019

See all articles by Kshitiz Gupta

Kshitiz Gupta

University of Petroleum and Energy Studies (UPES)

Prayas Sharma

University of Petroleum and Energy Studies (UPES)

C. N. Bouza

University of Havana - Department of Applied Mathematics

Date Written: April 24, 2018

Abstract

English Abstract: 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.

Portuguese Abstract: Las transacciones sociológicas cumplen un papel importante en el comportamiento humano y la posición social. El Titanic era la paradoja perfecta ya que los pasajeros pertenecían a grupos de altos ingresos, de ingresos medios y de bajos ingresos. Es interesante ver cómo los patrones en el sentido sociológico decidieron cómo iba a sobrevivir. Los datos fueron recolectados del sitio web “Kaggle.com” y se aplicaron algoritmos de aprendizaje automático después de un análisis visual y exploratorio. La hipótesis, las mujeres y los niños se salvaron y se hicieron famosos después de que la película Titanic de Steven Spielberg (1975) se pusiera a prueba mediante un algoritmo forestal aleatorio junto con la hipótesis de que la densidad familiar desempeñaba un papel importante en la supervivencia. El resultado enumeró ese título y el sexo fue el factor más importante que decidió la tasa de supervivencia de los pasajeros.

Keywords: Titanic, posición social, sobrevivientes, género, tamaño de la familia, social class, survived, sex, family size

JEL Classification: C02, C12, D91, Q59

Suggested Citation

Gupta, Kshitiz and Sharma, Prayas and Bouza, Carlos N., Surviving the Titanic Tragedy: A Sociological Study Using Machine Learning Models (Sobreviviendo a la tragedia del Titanic: un estudio sociológico utilizando modelos de aprendizaje automático) (April 24, 2018). Revista Suma de Negocios 9 (20) 86-92 2018. Available at SSRN: https://ssrn.com/abstract=3417433

Kshitiz Gupta (Contact Author)

University of Petroleum and Energy Studies (UPES) ( email )

Energy Acres
P.O. Bidholi via Premnagar,
Dehradun, IN Uttarakhand 248007
India

Prayas Sharma

University of Petroleum and Energy Studies (UPES) ( email )

Energy Acres
P.O. Bidholi via Premnagar,
Dehradun, IN Uttarakhand 248007
India

Carlos N. Bouza

University of Havana - Department of Applied Mathematics ( email )

Havana
Cuba

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