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 an advanced ensemble learning system combining Transformers and Convolutional Neural Networks for masked face recognition, achieving 94.22% accuracy through innovative stacking techniques. The project enhanced existing methodologies by implementing optimized weight distribution and stacked generalization, demonstrating significant improvements over state-of-the-art approaches. Notable achievements included a 2.2% accuracy gain over traditional ensemble methods and robust performance across multiple datasets.