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035 _a.b12345283
099 9 _aAIT RSPR no.IM-16-05
100 1 _aBakiev, Sabit Kenjebaevich
245 1 0 _aData mining techniques for predicting the survival of passangers on the Titanic
260 _aPathum Thani, Thailand :
_bAsian Institute of Technology,
_c2016
300 _a30 p. :
_bill.
490 1 _aResearch studies project report ;
_vno. IM-16-05
500 _aA researchsubmitted in partial fulfillment of the requirements for thed egree of Masterof Science in Information Management, School of Engineering and Technology
502 _aResearch studies project report (M. Sc.) - Asian Institute of Technology, 2016
520 _aMining 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, Naive 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.
650 0 _aData mining
650 0 _aMachine learning
700 1 _aGuha, Sumanta,
_eChairperson
700 0 _aVatcharaporn Esichaikul,
_eExamination Committee
700 1 _aHuynh, Trung Luong,
_eExamination Committee
710 2 _aAsian Development Bank - Japan Scholarship Program (ADB-JSP),
_eScholarship donor
810 2 _aAsian Institute of Technology.
_tResearch studies project report ;
_vno. IM-16-05
856 _3Full-Text
_uhttp://203.159.5.9/ait-thesis/detail.php?q=B05037
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