Prediction of Survivors in the Titanic Cruise
1Rajesh M*, Computer Science and Engineering, Vellore Institute of Technology, Vellore, India.
Manuscript received on 15 August 2019. | Revised Manuscript received on 25 August 2019. | Manuscript published on 30 September 2019. | PP: 1268-1271 | Volume-8 Issue-3 September 2019 | Retrieval Number: C4408098319/19©BEIESP | DOI: 10.35940/ijrte.C4408.098319
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: 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.
Keywords: Machine Learning, Data Mining, Random Forest algorithm, Support Vector Machine, Gradient Boosting Machine.
Scope of the Article: Regression and Prediction