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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)

2018; RELX Group (Netherlands); Linguagem: Espanhol

1556-5068

Kshitiz Gupta, Prayas Sharma, Carlos N. Bouza,

Computational and Text Analysis Methods

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 sociologicas cumplen un papel importante en el comportamiento humano y la posicion social. El Titanic era la paradoja perfecta ya que los pasajeros pertenecian a grupos de altos ingresos, de ingresos medios y de bajos ingresos. Es interesante ver como los patrones en el sentido sociologico decidieron como iba a sobrevivir. Los datos fueron recolectados del sitio web “Kaggle.com” y se aplicaron algoritmos de aprendizaje automatico despues de un analisis visual y exploratorio. La hipotesis, las mujeres y los ninos se salvaron y se hicieron famosos despues de que la pelicula Titanic de Steven Spielberg (1975) se pusiera a prueba mediante un algoritmo forestal aleatorio junto con la hipotesis de que la densidad familiar desempenaba un papel importante en la supervivencia. El resultado enumero ese titulo y el sexo fue el factor mas importante que decidio la tasa de supervivencia de los pasajeros.