This project explores the relationship between average IQ and average GDP across different continents. Through rigorous data analysis, we uncover nuanced correlations and emphasize the need for region-specific approaches to address cognitive and economic dynamics. The findings offer valuable insights for policymakers, educators, and researchers, shaping evidence-based interventions and policy recommendations aimed at fostering cognitive and economic development.

This project investigates corporate bankruptcy prediction through machine learning, employing K-Nearest Neighbors, Decision Trees, and Random Forest models. Using financial ratios and governance indicators from companies between 1999 and 2009, the study assesses model performance to identify the most accurate approach for classifying bankruptcy risk.

This project analyzes factors affecting Mathematics performance in secondary school students. We apply statistical tests and machine learning models to assess influences like gender, relationship status, study time, and family background. The findings offer insights to support targeted educational interventions and improve student outcomes.