This work applies machine learning and statistical modeling to real-world prediction and inference problems across diverse domains, including social media influence, infrastructure reliability, pharmacological evaluation, and healthcare diagnostics. The studies identify key predictive and causal factors, incorporate uncertainty quantification, cost-aware decision-making, and robust missing-data handling, and systematically compare model performance across a range of methodological approaches.
