03402nas a2200457 a 4500 20260818085750.0 200710s2016 th uu m rtt 0| a1eng d .b12345283 AIT RSPR no.IM-16-05 Bakiev, Sabit Kenjebaevich Data mining techniques for predicting the survival of passangers on the Titanic Pathum Thani, Thailand : Asian Institute of Technology, 2016 30 p. : ill. Research studies project report ; no. IM-16-05 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 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. Data mining Machine learning Guha, Sumanta, Chairperson Vatcharaporn Esichaikul, Examination Committee Huynh, Trung Luong, Examination Committee Asian Development Bank - Japan Scholarship Program (ADB-JSP), Scholarship donor Asian Institute of Technology. Research studies project report ; no. IM-16-05 Full-Text http://203.159.5.9/ait-thesis/detail.php?q=B05037 .b12345283 mnait m 240418 0 200710 m a m 0 mnait mnait mnarc mnarc 40 61 20 Barcode : 30050120891287 CREATED : 2020-09-06 RECORD # : i13274065 LPATRON : 0 LCHKIN : - # RENEWALS : 0 # OVERDUE : 0 IUSE3 : 0 TOT CHKOUT : 0 TOT RENEW : 0 Barcode : - CREATED : 2020-09-06 RECORD # : i13274077 LPATRON : 0 LCHKIN : - # RENEWALS : 0 # OVERDUE : 0 IUSE3 : 0 TOT CHKOUT : 0 TOT RENEW : 0 Barcode : 30050120982888 CREATED : 2020-10-07 RECORD # : i13281410 LPATRON : 0 LCHKIN : - # RENEWALS : 0 # OVERDUE : 0 IUSE3 : 0 TOT CHKOUT : 0 TOT RENEW : 0 Barcode : 30050120982870 CREATED : 2020-10-07 RECORD # : i13281422 LPATRON : 0 LCHKIN : - # RENEWALS : 0 # OVERDUE : 0 IUSE3 : 0 TOT CHKOUT : 0 TOT RENEW : 0 25627 25627 0 0 0 0 MAIN MAIN mnarc 2026-08-18 0 AIT RSPR no.IM-16-05 30050120891287 2026-08-18 08:57:50 1 2026-08-18 40 0 0 0 0 MAIN MAIN mnarc 2026-08-18 0 AIT RSPR no.IM-16-05 2026-08-18 08:57:50 2026-08-18 61 0 0 0 0 MAIN MAIN mnait 2026-08-18 50.00 0 AIT RSPR no.IM-16-05 30050120982888 2026-08-18 08:57:50 1 2026-08-18 20 0 0 0 0 MAIN MAIN mnait 2026-08-18 50.00 0 AIT RSPR no.IM-16-05 30050120982870 2026-08-18 08:57:50 2 2026-08-18 20