Predictive Modeling and Decision-Making in Data Science: A Comparative Study

The study presents a comprehensive investigation into the intricate relationship between predictive modelling techniques, model interpretability, and ethical considerations in data-driven decision-making. The research design integrates quantitative and qualitative analyses. Model performances are assessed using accuracy, precision, recall, F1-Score, and AUC-ROC metrics on distinct datasets. Furthermore, techniques such as Local Interpretable Model-agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP) are employed to enhance model interpretability. Ethical and social implications are examined through bias assessment, privacy evaluation, and transparency measures. The outcomes highlight the variability in model performance across datasets and decision-making scenarios. Neural Networks consistently exhibit competitive accuracy and F1-Scores. The interpretability techniques illuminate feature contributions, allowing stakeholders to comprehend complex model decisions. Ethical analysis reveals the presence of minor gender bias and showcases the efficacy of privacy-preserving methods. This study serves as a comprehensive guide for practitioners, policymakers, and organisations seeking to harness the power of predictive modelling in decision-making.