Analysis and detection of Titanic survivors using generalized linear models and decision tree algorithm

Abstract

In the article, it is aimed to investigate the factors affecting survival in today's legendary giant accident with different methods. The analysis aims to find the method that best determines survival. For this purpose, logit and probit models from generalized linear models and random tree algorithm from decision tree methods were used. The study was carried out in two stages. Firstly; in the analysis made with generalized linear models, variables that did not contribute significantly to the model were determined. Classification accuracy was found to be 79.89% for the logit model and 79.04% for the probit model. In the second stage; classification analysis was performed with random tree decision trees. Classification accuracy was determined to be 77.21%. In addition; according to the results obtained from the generalized linear models, the classification analysis was repeated by removing the data that made meaningless contribution to the model. The classification rate increased by 4.36% and reached 81.57%. After all; It was determined that the decision tree analysis made with the variables extracted from the model gave better results than the analysis made with the original variables. These results are thought to be useful for researchers working on classification analysis. In addition, the results can be used for purposes such as data preprocessing, data cleaning.

Keywords

• decision tree
• generalized linear models
• logit regression
• probit regression
• random tree

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