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Data Mining Techniques for Predicting the Survival of Passengers of The Titanic

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dc.contributor.advisor Sumanta, Guha Prof
dc.contributor.author Bakiev, Sabit
dc.contributor.other Esichaikul, Vatcharaporn Dr
dc.contributor.other Huynh Trung, Luong Dr
dc.date.accessioned 2016-04-18T04:20:35Z
dc.date.available 2016-04-18T04:20:35Z
dc.date.issued 2016-03-21
dc.identifier.citation Kansas State University en_US
dc.identifier.uri http://www.cs.ait.ac.th/xmlui/handle/123456789/812
dc.description 30 p. en_US
dc.description.abstract Mining techniques have proven to be effective in exploring data. In this report, the efficiency of several data-mining methods is explored. In particular, we apply these methods to the predictive modelling competition Titanic: Machine Learning from Disaster currently active at kaggle.com, a website for such competitions. This particular competition is a classification challenge to build a model to predict which passengers on the Titanic survived. The focus of our approach is comparing different data-mining techniques such as K-neighbourhood,Logistic Regression, Support Vector Machine, XGBoost, Linear Regression, Stochastic Gradient Decent, Decision Tree, Naïve Bayes and Random Forest algorithms. Results indicate that the predictors’ gender, ticket price, embarked port, age, title, and passenger class are the most important variables to predict survival of the passengers. According to the results,Random Forest classifier has gained the highest accuracy of nine classifiers with a score:“0.80861” (322 out of 3667) top 10% on the Titanic: Machine Learning from Disaster Competition. en_US
dc.description.sponsorship Asian Development Bank- Japan Scholarship Program (ADB-JSP) en_US
dc.language.iso en en_US
dc.publisher AIT en_US
dc.subject Machine Learning en_US
dc.subject Data Mining en_US
dc.subject Classification en_US
dc.title Data Mining Techniques for Predicting the Survival of Passengers of The Titanic en_US
dc.type Research report en_US

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