This work applies machine learning and statistical modeling to real-world prediction problems involving social media influence, infrastructure reliability, and historical survival outcomes. The studies identify key predictive factors and compare model performance. The results highlight how data-driven approaches can uncover important drivers of outcomes, such as language accessibility in TED talk popularity, meteorological conditions in pipeline failures, and demographic characteristics in survival during the Titanic disaster.
