Analysis of Machine Learning Algorithms for Predicting Titanic Disaster Survival Rate

This research investigates the factors that influenced survival rates among passengers on the Titanic, utilizing machine learning algorithms to analyze a dataset of 891 individuals. By examining attributes such as Age, Passenger class, Sex, Name, Embarked, and Fare, the study aims to identify correlations that may have contributed to higher survival probabilities. Various machine learning algorithms, including Logistic Regression, CatBoost, Naive Bayes, Random Forest, and Linear SVM, were implemented and evaluated on the dataset. The results demonstrate that the CatBoost algorithm outperformed the others, achieving the highest accuracy rate of 78.41<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sup>/0. Additionally, CatBoost exhibited superior performance in terms of True Positive and False Negative values, indicating its effectiveness in predicting survival. This research provides valuable insights into the factors that influenced survival on the Titanic and contributes to the understanding of historical events through the application of machine learning techniques.