This paper presents a comparative study evaluating the effectiveness of various supervised learning algorithms for classification tasks, focusing on the renowned “Titanic” dataset. The study encompasses data preprocessing, feature extraction and selection, model training, and performance evaluation using the accuracy metric. The research highlights the superior performance of Decision Tree and Random Forest algorithms, achieving the highest accuracy of 89.56% in predicting passenger survival, showcasing their ability to capture complex data relationships. While Logistic Regression, SVM, and Linear SVC attained lower accuracy scores, their results still provide valuable insights for analysis. This underscores the importance of considering multiple algorithms and comparing their performance. The findings contribute to a deeper understanding of classification algorithms and their applicability to real-world problems. The paper concludes by outlining future research directions, including exploring additional evaluation metrics, hyperparameter tuning, and applying the methodology to other datasets for broader generalization.