TY  - SER
AU  - Bakiev,Sabit Kenjebaevich
AU  - Guha,Sumanta
AU  - Vatcharaporn Esichaikul,
AU  - Huynh,Trung Luong
ED  - Asian Development Bank - Japan Scholarship Program (ADB-JSP),
TI  - Data mining techniques for predicting the survival of passangers on the Titanic
T2  - Research studies project report
PY  - 2016///
CY  - Pathum Thani, Thailand
PB  - Asian Institute of Technology
KW  - Data mining
KW  - Machine learning
N1  - A researchsubmitted in partial fulfillment of the requirements for thed egree of Masterof Science in Information Management, School of Engineering and Technology; Research studies project report (M. Sc.) - Asian Institute of Technology, 2016
N2  - 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, 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.
UR  - http://203.159.5.9/ait-thesis/detail.php?q=B05037
ER  -