Developed a comprehensive machine learning system for predicting Airbnb prices in New York City, achieving 62.87% accuracy on test data using XGBoost. The project incorporated extensive exploratory data analysis, feature engineering, and model optimization, comparing various regression algorithms including classical ML models and ensemble methods.

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.