Data mining techniques for predicting the survival of passengers on the Titanic | |
| Author | Bakiev, Sabit Kenjebaevich |
| Call Number | AIT RSPR no.IM-16-05 |
| Subject(s) | Data mining Machine learning |
| Note | A research submitted in partial fulfilment of the requirements for the degree of Master of Science in Information Management, School of Engineering and Technology |
| Publisher | Asian Institute of Technology |
| Series Statement | Research studies project report ; no. IM-16-05 |
| 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. |
| Year | 2016 |
| Corresponding Series Added Entry | Asian Institute of Technology. Research studies project report ; no. IM-16-05 |
| Type | Research Study Project Report (RSPR) |
| School | School of Engineering and Technology (SET) |
| Department | Department of Information and Communications Technologies (DICT) |
| Academic Program/FoS | Information Management (IM) |
| Chairperson(s) | Guha, Sumanta; |
| Examination Committee(s) | Vatcharaporn Esichaikul;Huynh Trung Luong; |
| Scholarship Donor(s) | Asian Development Bank- Japan Scholarship Program (ADB-JSP); |
| Degree | Research studies project report (M. Sc.) - Asian Institute of Technology, 2016 |