A Comparative Algorithm Study Using Titanic Dataset for Best Accurate Prediction

The Titanic is a British cruise ship that is claimed to be the largest ship ever built in world history. The ship struck an iceberg on its maiden voyage across the Atlantic in 1912 from Southampton to New York. Of the more than $\mathbf{2. 2 0 0}$ passengers, almost half died in this tragic accident. This famous incident prompted scientists to research more about the event. This research aims to analyze data and understand the factors that influence passenger survival. Using Machine Learning techniques and a data set consisting of 891 records, researchers tried to determine the relationship between factors such as age, gender, passenger class, and ticket price. This research uses various machine learning algorithms such as Random Forest, Optimize Selection, and Optimize Features to predict the accuracy of each algorithm. These factors may influence passenger survival rates. Specifically, this research aims to compare algorithms based on the percentage accuracy of test data. The research results show that the highest accuracy is achieved by the Random Forest algorithm with weight optimization using Particle Swarm Optimization (PSO).