Casualty on the Titanic based on Machine Learning Methods

The Titanic sank on April 15, 1914, with 2224 people on board, and only 32% survived. The survivors are somewhat random, but they are somewhat the same. Studying the types of people who are more likely to survive in a disaster will promote an understanding of the values and ideology of the society at the time. Therefore, this study collected data on some of the passengers on the Titanic and the survivors through Kaggle. This study predicts the survival of passengers through data cleaning, feature engineering, dimensionality reduction processing, and different models such as Random Forest, Decision Tree, KNN, and Logistic regression. In this study, it was found that Age, gender, representation of socioeconomic status, and whether they traveled alone were associated with survival rates. Among them, the decision tree and random forest models scored 98.65, which performed the best and had the highest prediction accuracy. After visualizing the data, this study concluded that survival rates were higher for females, children younger than ten years old first-class passengers, and non-solitary travelers.