This study investigates methods for predicting credit card defaults using machine learning techniques optimized for big data environments. Accurate predictions are vital for financial institutions to manage risk and optimize lending strategies at scale. We apply deep learning models and fine-tuned machine learning algorithms, focusing on feature representation learning and normalization to handle large datasets effectively. Several models, including Logistic Regression, Random Forest, and Neural Networks, were implemented with hyperparameter tuning and advanced feature engineering. Our results highlight the importance of model optimization and feature learning in improving credit risk prediction for big data, offering insights into the factors driving default risk and advancing predictive analytics in this domain.

The Israeli-Palestinian conflict is complex and longstanding. This study uses textual analysis through the lens of moral foundations theory to explore how moral values, emotional expressions, and political ideologies are reflected in tweets about the Israeli-Palestine Conflict. By analyzing public discourse and social media interactions, the study seeks to uncover the underlying moral frameworks and emotional responses that shape public perspectives on this ongoing conflict. The results reveal three key findings: 1) both anti-Israel and anti-Hamas tweets emphasize conflict, war, and human rights concerns, characterized by strong negative emotions; 2) anti-Hamas tweets exhibit higher emotional intensity, particularly around specific violent incidents and key figures; and 3) anti-Israel tweets encompass a broader range of issues, such as campus protests and anti-Semitism, with a focus on Israel’s policies and actions from a moral and human rights perspective. This study combines emotion and moral framework analysis, which is rarely used for war related discourse. By combining these perspectives, the research provides valuable theoretical and practical insights that deepen our understanding of how moral and emotional factors shape public opinion on the Israeli-Palestinian conflict.