Survivals of Titanic Prediction Utilizing Tree-based Machine Learning Models

Abstract

The shipwreck of Titanic is a well-known tragedy. Although it happened more than a century ago, researchers are still investigating the patterns of the survivors to gain more insight into human behaviors in catastrophes. This paper adopts machine learning techniques, including decision tree, random forest, and gradient boosting, to conduct a binary classification to predict whether a person survived. The selected models are all tree-based, making it convenient to examine the importance of features. In the preprocessing stage, all numerical features are discretized. This paper first investigates the performances of the models. Subsequently, the model with the best performance generates and studies the importance of the feature. The result demonstrates that the decision tree classifier with a max depth equal to seven achieves the highest accuracy of 0.78. The results of the three models are similar, indicating that the research is robust. The feature importance generated by the decision tree classifier shows that sex and social status significantly impact the survival result. In addition, whether the person is a child also makes a difference. The discretized features do not have enough influence on the result of survival. This paper concludes that the tunned decision tree classifier is the best model to study the features in this paper, but the created features are not effective enough.