Comparative Analysis of Titanic Data Prediction Using Logistic Regression and Support Vector Machine for Enhanced Accuracy

In the context of evaluating machine learning algorithms for binary classification tasks, predicting passenger survival on the Titanic provides a practical benchmark. The research aims to determine which machine learning algorithm is better at predicting passenger survival on the Titanic. Both algorithms were tested on the same dataset, with sample sizes determined using G-power software. The sample size was set at 10 per group, with a pretest power of 80%, a threshold of 0.05%, and a confidence interval of 95%. The results showed that Logistic Regression outperformed Support Vector Machine in predicting survival rates. Specifically, Logistic Regression achieved an accuracy of 92.80%, whereas Support Vector Machine reached 86.25%. The disparity in output between the two methods was statistically significant, with a p-value of 0.003 (which is less than 0.05).