Developed a sophisticated network analysis system for S&P 500 stocks (2018-2022), employing advanced graph theory and machine learning techniques to visualize and analyze market interconnectivity patterns. The project revealed crucial insights into stock market behavior during the COVID-19 pandemic, identifying influential stocks and sector-based communities through correlation analysis and network metrics. Notable findings included the detection of scale-free properties in market networks and the quantification of sector-based trading patterns using Jaccard similarity coefficients.

Developed a sophisticated predictive analytics system for charity fundraising campaign “SJ22”, analysing donation patterns of 79,469 donors to optimise solicitation strategies. The project utilised advanced statistical modelling to predict donation likelihood and amounts, enabling data-driven decisions for maximising campaign profitability through targeted solicitation.