Open AccessPrediction of Survivors in the Titanic Cruise
Author(s) -
Rajesh M*
Publication year - 2019
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.c4408.098319
Subject(s) - cruise , random forest , computer science , boosting (machine learning) , data set , machine learning , artificial intelligence , data science , gradient boosting , competition (biology) , support vector machine , set (abstract data type) , operations research , data mining , engineering , geology , oceanography , ecology , biology , programming language
On the 15th of April, 1912 the titanic witnessed a disaster resulting in the sinking of her passengers on the maiden voyage near North Atlantic. Even though it is a very long time since this maritime disaster took place, the idea behind what impacts each individual survival is still a great research attracting researcher’s attention. The approach taken in this paper is to utilize the publically available data set from website called Kaggle. Kaggle is a popular data science webpage that put together information of people in the titanic into a data set for the data mining competition: “Titanic: Machine Learning from Disaster”. The research and comparisons in this paper uses a few machine learning techniques and algorithms to analyse the data for classification and prediction of survivors. The prediction and efficiency of these algorithms depend greatly on data analysis and model. The techniques used to do so are Random Forest, Support Vector Machine, Gradient Boosting Machine.